The following is a conversation with David Patterson, touring award winner and professor

of computer science at Berkeley. He is known for pioneering contributions to risk processor

architecture used by 99% of new chips today and for cocreating RAID storage. The impact that these

two lines of research and development have had in our world is immeasurable. He is also one of the

great educators of computer science in the world. His book with John Hennessey is how I first learned

about and was humbled by the inner workings and machines at the lowest level.

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robotics system education for young people around the world. And now here's my conversation with David

Patterson. Let's start with the big historical question. How have computers changed in the past

50 years at both the fundamental architectural level and in general in your eyes? Well, the biggest

thing that happened was the invention of the microprocessor. So computers that used to fill up

several rooms could fit inside your cell phone. And not only and not only did they get smaller,

they got a lot faster. So they're million times faster than they were 50 years ago. And they're

much cheaper and they're ubiquitous. You know, there's 7.8 billion people on this planet.

Probably half of them have cell phones right now. Just remarkable.

There's probably more microprocessors than there are people. Sure. I don't know what the ratio is,

but I'm sure it's above one. Maybe it's 10 to one or some number like that.

What is a microprocessor? So a way to say what a microprocessor is, is to tell you what's inside

a computer. So a computer forever has classically had five pieces. There's input and output,

which kind of naturally, as you'd expect, is input is like speech or typing and output is displays.

There's a memory and like the name sounds, it remembers things. So it's integrated circuits,

whose job is you put information in, then when you ask for it, it comes back out. That's memory.

And the third part is the processor, where the team microprocessor comes from. And that has two

pieces as well. And that is the control, which is kind of the brain of the processor and the

what's called the arithmetic unit. It's kind of the brawn of the computer. So if you think of the,

as a human body, the arithmetic unit, the thing that does the number crunching is the body and

the control is the brain. So those five pieces, input, output, memory, arithmetic unit and control

are have been in computer since the very dawn. And the last two are considered the processor.

So a microprocessor simply means a processor that fits on a microchip. And that was invented about,

you know, 40 years ago, was the first microprocessor. It's interesting that you refer to the arithmetic

unit as the like you connected to the body and the controllers, the brain. So I guess,

I never thought of it that way. The nice way to think about it, because most of the actions,

the microprocessor does in terms of literally sort of computation, but the microprocessor does

computation, it processes information. And most of the thing it does is basic arithmetic operations.

What are the operations, by the way? It's a lot like a calculator. So there are

add instructions, subtract instructions, multiply and divide. And kind of the brilliance of the

invention of the microprocessor or the processor is that it performs very trivial operations,

but it just performs billions of them per second. And what we're capable of doing is writing software

that can take these very trivial instructions and have them create tasks that can do things better

than human beings can do today. Just looking back through your career, did you anticipate the kind

of how good we would be able to get at doing these small basic operations? How many surprises along

the way? We just kind of said back and said, wow, I didn't expect it to go this fast, this good.

Well, the fundamental driving force is what's Gordon Moore's law, which was named after

Gordon Moore, who's a Berkeley illness. And he made this observation very early in what are called

semiconductors. And semiconductors are these ideas. You can build these very simple switches,

and you can put them on these microchips. And he made this observation over 50 years ago. He

looked at a few years and said, I think what's going to happen is the number of these little

switches called transistors is going to double every year for the next decade. And he said this

in 1965. And in 1975, he said, well, maybe it's going to double every two years. And that what

other people since named that Moore's law guided the industry. And when Gordon Moore made that

prediction, he wrote a paper back in, I think, in the 70s and said, not only did this going to happen,

he wrote, what would be the implications of that? And in this article from 1965, he shows ideas like

computers being in cars and computers being in something that you would buy in the grocery store

and stuff like that. So he kind of not only called his shot, he called the implications of it.

So if you were in in the computing field, and if you believed Moore's prediction, he kind of said

what the what would be happening in the future. So so it's not kind of it's at one sense, this

is what was predicted. And you could imagine it was easy to believe that Moore's law was going to

continue. And so this would be the implications. On the other side, there are these shocking events

in your life, like I remember driving in the marine across the Bay in San Francisco and seeing a

bulletin board at a local civic center and had a URL on it. And it was like for all for all for

the people at the time, these first URLs, and that's the, you know, WWW select stuff with the

HTTP, people thought it was look like alien, alien writing, right, they'd see these advertisements

and commercials or bulletin boards that had this alien writing on it. So for the lay people was

like, what the hell is going on here? And for those people in industry, it's Oh my God,

this stuff is getting so popular, it's actually leaking out of our nerdy world and into the real

world. So that I mean, there was events like that. I think another one was, I remember with the early

days of the personal computer, when we started seeing advertisements in magazines for personal

computers, like it's so popular that it's made the newspaper. So at one hand, you know, Gordon Moore

predicted it and you kind of expected it to happen. But when it really hit and you saw it

affecting society, it was, it was shocking. So maybe taking a step back and looking both

the engineering and philosophical perspective, what, what do you see as the layers of abstraction

in the computer? Do you see a computer as a set of layers of abstractions? Yeah, I think that's

one of the things that computer science fundamentals is the, these things are really complicated

in the way we cope with complicated software and complicated hardware is these layers of

abstraction. And that simply means that we, you know, suspend disbelief and pretend that the only

thing you know is that layer, and you don't know anything about the layer below it. And that's the

way we can make very complicated things. And probably it started with hardware, that that's

the way it was done. But it's been proven extremely useful. And, you know, I would say in a modern

computer today, there might be 10 or 20 layers of abstraction. And they're all trying to kind of

enforce this contract is all you know is this interface, there's a set of commands that you

can are allowed to use, and you stick to those commands, and we will faithfully execute that.

And it's like peeling the air layers of a London of an onion, you get down, there's a new set of

layers and so forth. So for people who want to study computer science, the exciting part about

it is you can keep peeling those layers, you take your first course, and you might learn to program

in Python, and then you can take a follow on course, and you can get it down to a lower level

language like C, and, you know, you can go and then you can, if you want to, you can start getting

into the hardware layers, and you keep getting down all the way to that transistor that I talked

about that Gordon Moore predicted, and you can understand all those layers all the way up to the

highest level application software. So it's a very kind of magnetic field. If you're interested,

you can go into any depth and keep going. In particular, what's happening right now, or it's

happened in software the last 20 years recently in hardware, there's getting to be open source

versions of all of these things. So what open source means is what the engineer, the programmer

designs, it's not secret, the belonging to a company, it's out there on the worldwide web,

so you can see it. So you can look at, for lots of pieces of software that you use, you can see

exactly what the programmer does if you want to get involved. That used to stop at the hardware.

Recently, there's been an efforts to make open source hardware and those interfaces open, so you

can see that. So instead of before you had to stop at the hardware, you can now start going

layer by layer below that and see what's inside there. So it's a remarkable time that for the

interested individual can really see in great depth what's really going on in the computers that

power everything that we see around us. Are you thinking also when you say open source at the

hardware level, is this going to the design architecture instruction set level, or is it

going to literally the manufacturer of the actual hardware, of the actual chips, whether that's

ASIC specialized a particular domain or the general? Yeah, so let's talk about that a little bit.

So when you get down to the bottom layer of software, the way software talks to hardware is

in a vocabulary. And what we call that vocabulary, we call that the words of that vocabulary called

instructions. And the technical term for the vocabulary is instruction set. So those instructions

are likely talked about earlier. There can be instructions like add, subtract, and multiply,

divide. There's instructions to put data into memory, which is called a store instruction,

and to get data back, which is called a load instructions. And those simple instructions

go back to the very dawn of computing. And in 1950, the commercial computer had these

instructions. So that's the instruction set that we're talking about. So up until I'd say 10 years

ago, these instruction sets are all proprietary. So a very popular one is owned by Intel, the one

that's in the cloud and in all the PCs in the world. Intel owns that instruction set, it's

referred to as the x86. There've been a sequence of ones that the first number was called 8086.

And since then, there's been a lot of numbers, but they all ended in 86. So there's been that

kind of family of instruction sets. And that's proprietary. And that's proprietary. The other one

that's very popular is from ARM that kind of powers all the cell phones in the world, all the

iPads in the world, and a lot of things that are so called Internet of Things devices. ARM and that

one is also proprietary. ARM will license it to people for a fee, but they own that. So the new

idea that got started at Berkeley kind of unintentionally 10 years ago is in early in my

career, we pioneered a way to do these vocabulary instruction sets that was very controversial

at the time. At the time in the 1980s, conventional wisdom was these vocabulary instruction sets

should have, you know, powerful instructions. So polysyllabic kind of words, you can think of

that. And so that instead of just add subtractive multiply, they would have polynomial divide or

sort a list. And the hope was of those powerful vocabularies that make it easier for software.

So we thought that didn't make sense for microprocessors. There was people at Berkeley and

Stanford and IBM who argued the opposite. And what we called that was a reduced instruction set

computer. And the abbreviation was RISC and typical for computer people, we use the abbreviations

that are pronouncing it. So risk was the thing. So we said for microprocessors, which with Gordon's

more is changing really fast, we think it's better to have a pretty simple set of instructions,

reduced set of instructions, that that would be a better way to build microprocessors, since

they're going to be changing so fast due to Moore's law. And then we'll just use standard

software to cover the use generate more of those simple instructions. And one of the pieces of

software that it's in that software stack, going between these layers of abstractions is called

a compiler. And it's basically translates. It's a translator between levels, we said the translator

will handle that. So the technical question was, well, since they're these reduced instructions,

you have to execute more of them. Yeah, that's right. But maybe execute them faster. Yeah,

that's right. They're simpler. So they go faster, but you have to do more of them. So what's

that tradeoff look like? And it ended up that we ended up executing maybe 50% more instructions,

maybe a third more instructions, but they ran four times faster. So this risk controversial risk

ideas proved to be maybe factors of three or four better. I love that this idea was controversial

and almost kind of like rebellious. So that's in the context of what was more conventional is the

complex instructional side computing. So how'd you pronounce that? Sisk. Sisk versus risk versus

sisk. And, and believe it or not, this sounds very, very, you know, who cares about this, right?

It was, it was violently debated at several conferences is like, what's the right way to go

is, is, and people thought risk was, you know, was a de evolution. We're going to make software

worse by making those instructions simpler and their fierce debates at several conferences in

the 1980s. And then later in the 80s, it kind of settled to these benefits. It's not completely

intuitive to me why risk has for the most part one. Yeah. So why did that happen? Yeah. Yeah.

And maybe I can sort of say a bunch of dumb things that could lay the land for further commentary.

So to me, this is a, this is kind of interesting thing. If you look at C++ versus C with modern

compilers, you really could write faster code with C++. So relying on the compiler to reduce

your complicated code into something simple and fast. So to me, comparing risk, maybe this is

a dumb question, but why is it that focusing the definition, the design of the instruction set on

very few simple instructions in the long run provide faster execution versus coming up with,

like you said, a ton of complicated instructions that over time, you know, years, maybe decades,

you come up with compilers that can reduce those into simple instructions for you.

Yeah. So let's try and split that into two pieces. So if the compiler can do that for you, if the

compiler can take, you know, a complicated program and produce simpler instructions,

then the programmer doesn't care, right? Programmer, yeah, I don't care. Just how, how fast is the

computer I'm using? How much does it cost? And so what we, what happened kind of in the software

industry is right around before the 1980s, critical pieces of software were still written

not in languages like C or C++. They were written in what's called assembly language,

where there's this kind of humans writing exactly at the instructions at the level

that a computer can understand. So they were writing add, subtract, multiply, you know,

instructions. It's very tedious. But the belief was to write this lowest level of software that

that people use, which are called operating systems, they had to be written in assembly language

because these high level languages were just too inefficient. They were too slow or the

the programs would be too big. So that changed with a famous operating system called Unix,

which is kind of the the grandfather of all the operating systems today. So the Unix demonstrated

that you could write something as complicated as an operating system in a language like C.

So once that was true, then that meant we could hide the instruction set from the programmer.

And so that meant then it didn't really matter. The programmer didn't have to write

lots of these simple instructions. That was up to the compiler. So that was part of our

arguments for risk is if you were still writing assembly language, there's maybe a better case

for Sys constructions. But if the compiler can do that, it's going to be, you know, that's done

once the computer translates at once. And then every time you run the program, it runs at this

this potentially simpler instructions. And so that that was the debate, right, is because

and people would acknowledge that the simpler instructions could lead to a faster computer.

You can think of monosyllabic instructions, you could say them, you know, if you think of reading,

you could probably read them faster or say them faster than long instructions. The same thing

that analogy works pretty well for hardware. And as long as you didn't have to read

a lot more of those instructions, you could win. So that's, that's kind of that's the basic idea

for risk. But it's interesting that the in that discussion of Unix and C, that there's only one

step of levels of abstraction from the code that's really the closest to the machine to the code

that's written by human. It's at least to me again, perhaps a dumb intuition, but it feels

like there might have been more layers, sort of different kinds of humans stacked at the top of

each other. So what's true and not true about what you said is several of the layers of software,

like so that if you hear two layers would be supposed to just talk about two layers, that

would be the operating system like you get from from Microsoft or from Apple like iOS or the

Windows operating system. And let's say applications that run on top of it like Word or Excel. So

both the operating system could be written in C. And the application could be written in C. So

but you could construct those two layers. And the applications absolutely do call upon the

operating system. And the change was that both of them could be written in higher level languages.

So it's one step of a translation, but you can still build many layers of abstraction

of software on top of that. And that's how things are done today. So

still today, many of the layers that you'll deal with, you may deal with debuggers, you may deal

with linkers. There's libraries, many of those today will be written in C++ say, even though that

language is pretty ancient. And even the Python interpreter is probably written in C or C++.

So lots of layers there are probably written in these some old fashioned efficient languages that

still take one step to produce these instructions, produce risk instructions, but they're composed

each layer of software invokes one another through these interfaces. And you can get

it 10 layers of software that way. So in general, the risk was developed here, Berkeley?

It was kind of the three places that were these radicals that advocated for this against the

rest of the community were IBM, Berkeley and Stanford. You're one of these radicals. And

how radical did you feel? How confident did you feel? How doubtful were you that risk might be

the right approach? Because it may you can also into it that is kind of taking a step back into

simplicity, not forward into simplicity. Yeah, no, it was easy to make. Yeah, it was easy to

make the argument against it. Well, this was my colleague, John Hennessy at Stanford nine, we

were both assistant professors. And for me, I just believed in the power of our ideas. I thought

what we were saying made sense, Morse law is going to move fast. The other thing that I didn't

mention is one of the surprises of these complex instruction sets. You could certainly write

these complex instructions if the programmers writing them themselves. It turned out to be

kind of difficult for the compiler to generate those complex instructions kind of ironically,

you'd have to find the right circumstances that that just exactly fit this complex instruction.

It was actually easier for the compiler to generate these simple instructions. So

not only did these complex instructions make the hardware more difficult to build,

often the compiler wouldn't even use them. And so it's harder to build the compiler doesn't use

them that much. The simple instructions go better with Morse law, that you know, the number of

transistors is doubling every every two years. So we're going to have, you know, the you want to

reduce the time to design the microprocessor that may be more important than these number of instructions.

So I think we believed in the that we were right, that this was the best idea. Then the question

became in these debates, well, yeah, that's a good technical idea. But in the business world,

this doesn't matter. There's other things that matter. It's like arguing that if there's a

standard with the railroad tracks, and you've come up with a better with, but the whole world

is covered in railroad tracks. So you'll, your ideas have no chance of success commercial success.

It was technically right. But commercially, it'll be insignificant. Yeah, it's kind of sad

that this world, the history of human civilization is full of good ideas that lost because somebody

else came along first with a worse idea. And it's good that in the computing world, at least

some of these have well, while you could, I mean, there's probably still cis people that say,

yeah, there still are. And what happened was what was interesting, Intel, a bunch of the cis

companies with cis instruction sets of vocabulary, they gave up, but not Intel. What Intel did

to its credit, because Intel's vocabulary was in the in the personal computer. And so that was a

very valuable vocabulary, because the way we distribute software is in those actual instructions.

It's in the instructions of that instruction set. So they, you don't get that source code,

what the programmers wrote, you get after it's been translated into the list level,

that's if you were to get a floppy disk or download software, it's in the instructions of

that instruction set. So the x86 instruction set was very valuable. So what Intel did cleverly,

and amazingly, is they had their chips in hardware do a translation step, they would take these

complex instructions and translate them into essentially in risk instructions in hardware

on the fly, you know, at at gigahertz clock speeds. And then any good idea that risk people had,

they could use, and they could still be compatible with this, with this really valuable PC software

software base, and which also had very high volumes, you know, 100 million personal computers

per year. So the CISC architecture in the business world was actually one in this PC era.

So just going back to the time of designing risk. When you design an instruction set

architecture, do you think like a programmer? Do you think like a microprocessor engineer?

Do you think like a artist, a philosopher? Do you think in software and hardware? I mean,

is it art? Is it science? Yeah, I'd say I think designing a good instruction set is an art.

And I think you're trying to balance the the simplicity and speed of execution

with how well easy it will be for compilers to use it, right? You're trying to create an

instruction set that everything in there can be used by compilers. There's not things that are

missing, that'll make it difficult for the program to run, they run efficiently, but you want it to

be easy to build as well. So it's that kind of, so you're thinking, I'd say you're thinking

hardware, trying to find a hardware software compromise that'll work well. And, and it's,

you know, it's, you know, it's a matter of taste, right? It's kind of fun to build instruction sets.

It's, it's not that hard to build an instruction set, but to build one that catches on and people

use, you know, you have to be, you know, fortunate to be the right place in the right time or have

a design that people really like. Are you using metrics? So is it quantifiable? Because you kind

of have to anticipate the kind of programs that people write ahead of time. So is that,

can you use numbers? Can you use metrics? Can you quantify something ahead of time? Or is this,

again, that's the hard part where you're kind of no, it's a big, a big change, kind of what happened.

I think from Hennessy's and my perspective in the 1980s, what happened was going from kind of

really, you know, taste and hunches to quantifiable. And in fact, he and I wrote a textbook at the

end of the 1980s called computer architecture, a quantitative approach. I heard of that. And,

and it's, it's the thing that it had a pretty big big impact in the field because we went from

textbooks that kind of listed. So here's what this computer does. And here's the pros and cons.

And here's what this computer does in pros and cons to something where there were formulas

and equations where you could measure things. So specifically for instruction sets, what

we do in some other fields do is we agree upon a set of programs, which we call benchmarks.

And a suite of programs. And then you develop both the hardware and the compiler and you get

numbers on how well your, your computer does, given its instruction set and how well you

implemented it in your microprocessor and how good your compilers are. And in computer architecture,

we, you know, using professors terms, we grade on a curve rather than grade on an absolute scale.

So when you say, you know, this, these programs run this fast, well, that's kind of interesting,

but how do you know it's better while you compare it to other computers at the same time? So the

best way we know how to make, turn it into a kind of more science and experimental and quantitative

is to compare yourself to other computers of the same era that have the same access, the same kind

of technology on commonly agreed benchmark programs. So maybe to toss up two possible

directions, we can go one is what are the different tradeoffs in designing architectures? We've been

already talking about Cisco risk, but maybe a little bit more detail in terms of specific

features that you were thinking about. And the other side is, what are the metrics that you're

thinking about when looking at these tradeoffs? Yeah, let's talk about the metrics. So during

these debates, we actually had kind of a hard time explaining convincing people the ideas. And

partly we didn't have a formula to explain it. And a few years into it, we hit upon a formula

that helped explain what was going on. And I think if we can do this, see how it works orally.

So the, if I can do a formula orally, let's see. So the, so fundamentally, the way you measure

performance is how long does it take a program to run program? If you have 10 programs, and typically

these benchmarks were sweet, because you'd want to have 10 programs so they could represent lots

of different applications. So for these 10 programs, how long did it take to run? When now,

when you're trying to explain why it took so long, you could factor how long it takes a program to

run into three factors. One of the first one is how many instructions did it take to execute?

So that's the, that's the, what we've been talking about, you know, the instructions of

okay, how many did it take? All right. The next question is how long did each instruction take

to run on average? So you'd multiply the number of instructions times how long it took to run.

And that's your time. Okay, so that's, but now let's look at this metric of how long did it

take the instruction to run? Well, it turns out the way we could build computers today is they

all have a clock. And you've seen this when you, if you buy a microprocessor, it'll say

3.1 gigahertz or 2.5 gigahertz and more gigahertz is good. Well, what that is, is the speed of the

clock. So 2.5 gigahertz turns out to be four billions of instruction or four nanoseconds. So

that's the clock cycle time. But there's another factor which is what's the average number of

clock cycles that takes per instruction. So it's number of instructions, average number of clock

cycles in the clock cycle time. So in these risk system debates, we would, we, they would concentrate

on, but risk needs to take more instructions. And we'd argue what maybe the clock cycle is faster,

but what the real big difference was, was the number of clock cycles per instruction.

Per instruction, that's fascinating. What about the mess of the beautiful mess of parallelism

in the whole picture? Parallelism, which has to do with say, how many instructions could execute

in parallel and things like that. You could think of that as affecting the clock cycles per

instruction because it's the average clock cycles per instruction. So when you're running a program,

if it, if it took 100 billion instructions and on average, it took two clock cycles per instruction

and they were four nanoseconds, you could multiply that out and see how long it took to run. And there's

all kinds of tricks to try and reduce the number of clock cycles per instruction. But it turned out

that the way they would do these complex instructions is they would actually build what we would call

an interpreter in a simpler, a very simple hardware interpreter. But it turned out that for the SISC

instructions, if you had to use one of those interpreters, it would be like 10 clock cycles

per instruction where the risk instructions could be two. So there'd be this factor of five advantage

in clock cycles per instruction. We have to execute say 25 or 50% more instructions. So that's

where the wind would come. And then you could make an argument whether the clock cycle times are the

same or not. But pointing out that we could divide the benchmark results time per program into three

factors. And the biggest difference in risk and SISC was the clock cycles per, you execute a few

more instructions, but the clock cycles per instruction is much less. And that was what this

debate, once we made that argument, then people say, Oh, okay, I get it. And so we went from it was

outrageously controversial in, you know, 1982, that maybe probably by 1984. So people said, Oh,

yeah, technically, they've got a good argument. What are the instructions in the risk instruction

set? Just to get an intuition. Okay, 1995, I was asked to sign to predict the future of what

microprocessor future. So I, and that, well, as I'd seen these predictions, and usually people

predict something outrageous just to be entertaining, right? And so my prediction for 2020 was,

you know, things are going to be pretty much, they're going to look very familiar to what they

are. And they are. And if you were to read the article, you know, the things I said are pretty

much true. The instructions that have been around forever are kind of the same.

And that's the outrageous prediction, actually, given how fast computers have been growing.

Well, you know, Morse law was going to go on, we thought for 25 more years, you know, who knows.

But kind of the surprising thing, in fact, you know, Hennessy and I, you know, won the ACM AM

Touring Award for both the risk instruction set contributions and for that textbook I mentioned.

But, you know, we're surprised that here we are 35, 40 years later after we did our work.

And the conventionalism of the best way to do instruction sets is still those risk

instruction sets that looked very similar to what we looked like, you know, we did in the 1980s.

So those, surprisingly, there hasn't been some radical new idea, even though we have,

you know, a million times as many transistors as we had back then.

But what are the basic constructions and how did they change over the years? So

we're talking about addition, subtraction, these are the.

Okay. So the things that are in a calculator are in a computer. So any of the buttons that

are in the calculator in the computer. So the button, so if there's a memory function key,

and like I said, those are turns into putting something in memory is called a store,

bring something back to a load. Just a quick tangent. When you say memory, what does memory mean?

Well, I told you there were five pieces of a computer. And if you remember in a calculator,

there's a memory key. So you want to have intermediate calculation and bring it back

later. So you'd hit the memory plus key M plus maybe, and it would put that into memory. And

then you'd hit an RM like recurrent instruction, and then bring it back on the display. So you

don't have to type it. You don't have to write it down and bring it back again. So that's exactly

what memory is. If you can put things into it as temporary storage and bring it back when you need

it later. So that's memory and loads and stores. But the big thing, the difference between a computer

and a calculator is that the computer can make decisions. And in amazingly decisions are as

simple as, is this value less than zero? Or is this value bigger than that value? So there's

those instructions, which are called conditional branch instructions is what give computers all

its power. If you were in the early days of computing before what's called the general

purpose microprocessor, people would write these instructions kind of in hardware. And but it

couldn't make decisions. It would just, it would do the same thing over and over again.

With the power of having branch instructions, it can look at things and make decisions

automatically. And it can make these decisions, you know, billions of times per second. And

amazingly enough, we can get, you know, thanks to advanced machine learning, we can,

we can create programs that can do some things smarter than human beings can do.

But if you go down that very basic level, it's the instructions are the keys on the calculator,

plus the ability to make decisions of these conditional branch instructions.

And all decisions fundamentally can be reduced down to these branch instructions.

Yeah. So in fact, and so, you know, going way back in the stack back to, you know,

we did four risk projects at Berkeley in the 1980s, they did a couple at Stanford in the 1980s.

In 2010, we decided we wanted to do a new instruction set, learning from the mistakes of

those risk architecture of the 1980s. And that was done here at Berkeley, almost exactly 10 years

ago. And the people who did it, I participated, but other Krzysztof Sanovic and others drove it.

They called it risk five to honor those risk, the four risk projects of the 1980s.

So what is risk five involve? So risk five is another instruction set of vocabulary. It's

learned from the mistakes of the past, but it still has, if you look at the, there's a core

set of instructions, it's very similar to the simplest architectures from the 1980s. And the

big difference about risk five is it's open. So I talked early about proprietary versus open and

software. So this is an instruction set. So it's a vocabulary. It's not, it's not hardware.

But by having an open instruction set, we can have open source implementations, open source

processors that people can use. Where do you see that going? It's a really exciting possibility,

but you're just thinking the scientific American, if you were to predict 10, 20,

30 years from now, that kind of ability to utilize open source instruction set architectures like

risk five, what kind of possibilities might that unlock? Yeah. And so just to make it clear,

because this is confusing, the specification of risk five is something that's like in a textbook.

There's books about it. So that's what that's kind of defining an interface. There's also the way

you build hardware is you write it in languages, they're kind of like C, but they're specialized

for hardware that gets translated into hardware. And so these implementations of this specification

are what are the open source. So they're written in something that's called Verilog or VHDL,

but it's put up on the web just like that you can see the C plus plus code for Linux on the web.

So that's the open instruction set enables open source implementations of risk five.

They can literally build a processor using this instruction set. People are people are.

So what happened to us that the story was this was developed here for our use to do our research.

And we made it we licensed under the Berkeley software distribution license, like a lot of

things get licensed here. So other academics use it, they wouldn't be afraid to use it.

And then about 2014, we started getting complaints that we were using it in our research in our

courses. And we got complaints from people in industries. Why did you change your instruction

set between the fall and the spring semester? And well, we get complaints from industrial time.

Why the hell do you care what we do with our instruction set? And then when we talked to

them, we found out there was this thirst for this idea of an open instruction set architecture.

And they had been looking for one they stumbled upon ours at Berkeley thought it was boy,

this looks great. We should use this one. And so once we realized there is this need for an

open instruction set architecture, we thought, that's a great idea. And then we started supporting

it and tried to make it happen. So this was kind of we accidentally stumbled into this

and to this need and our timing was good. And so it's really taking off. There's

universities are good at starting things, but not good at sustaining things. So like Linux has

a Linux foundation, there's a risk five foundation that we started. There's there's an annual

conferences. And the first one was done, I think, January of 2015. And the one that was just last

December in it, you know, it had 50 people at it. And the one last December had, I know, 1700

people were at it and the company's excited all over the world. So predicting into the future,

you know, if we were doing 25 years, I would predict that risk five will be, you know,

possibly the most popular instruction set architecture out there, because it's a pretty

good instruction set architecture, and it's open and free. And there's no reason lots of people

shouldn't use it. And there's benefits just like Linux is so popular today compared to 20 years ago.

And, you know, the fact that you can get access to it for free, you can modify it, you can

improve it for all those same arguments. And so people collaborate to make it a better system

for everybody to use. And that works in software. And I expect the same thing will happen in hardware.

So if you look at the arm Intel MIPS, if you look at just the lay of the land,

and what do you think, just for me, because I'm not familiar, how difficult this kind of transition

would, how much challenges this kind of transition would entail. Do you see,

let me ask my dumb question in another way. No, that's, I know where you're headed.

Well, there's a bunch. I think the thing you point out, there's these very popular proprietary

instruction sets, the x86. And so how do we move to risk five potentially, in sort of,

in the span of five, 10, 20 years, a kind of unification. Given that the devices, the kind

of way we use devices, IOT, mobile devices, and the cloud keeps changing. Well, part of it,

a big piece of it is the software stack. And what right now, looking forward, there seem to be three

important markets. There's the cloud. And the cloud is simply companies like Alibaba and Amazon

and Google, Microsoft, having these giant data centers with tens of thousands of servers in

maybe a hundred of these data centers all over the world. And that's what the cloud is. So the

computer that dominates the cloud is the x86 instruction set. So the instruction, or the

instruction sets used in the cloud are the x86. Almost 100% of that today is x86.

The other big thing are cell phones and laptops. Those are the big things today. I mean, the PC

is also dominated by the x86 instruction set, but those sales are dwindling. You know, there's maybe

200 million PCs a year, and there's, is there one and a half billion phones a year? There's numbers

like that. So for the phones, that's dominated by ARM. And now, and a reason that I talked about

the software stacks, and that the third category is Internet of Things, which is basically embedded

devices, things in your cars and your microwaves everywhere. So what's different about those three

categories is for the cloud, the software that runs in the cloud is determined by these companies,

Alibaba, Amazon, Google, Microsoft. So they control that software stack. For the cell phones,

there's both for Android and Apple, the software they supply, but both of them have market places

where anybody in the world can build software. And that software is translated or, you know,

compiled down and shipped in the vocabulary of ARM. So that's what's referred to as binary

compatible, because the actual, it's the instructions are turned into numbers, binary numbers,

and shipped around the world. So. And so just a quick interruption. So ARM, what is ARM?

ARM is an instruction set like a risk based. Yeah, it's a risk based instruction set. It's a

proprietary one. ARM stands for Advanced Risk Machine. ARM is the name where the company is.

So it's a proprietary risk architecture. So, and it's been around for a while, and it's,

you know, surely the most popular instruction set in the world right now. Every year,

billions of chips are using the ARM design in this post PC era.

Was it one of the early risk adopters of the risk idea?

The first ARM goes back, I don't know, 86 or so. So Berkeley and Stanford,

did their work in the early 80s. The ARM guys needed an instruction set and they read our papers

and it heavily influenced them. So getting back to my story, what about Internet of Things? Well,

software is not shipped in Internet of Things. It's the embedded device people control that

software stack. So you would, the opportunities for risk five, everybody thinks is in the

Internet of Things embedded things, because there's no dominant player like there is in the

cloud or the smartphones. And, you know, it's, it's, doesn't have a lot of licenses associated with

and you can enhance the instruction set if you want. And it's, and people have looked at instruction

sets and think it's a very good instruction set. So it appears to be very popular there.

It's possible that in the cloud people, those companies control their software stacks.

So it's possible that they would decide to use risk five if we're talking about 10 and 20 years

in the future. The one of the harder would be the cell phones since people ship software

in the ARM instruction set that you'd think be the more difficult one. But if risk five really

catches on in, you know, you could, in a period of a decade, you can imagine that's changing over

too. Do you have a sense why risk five or ARM has dominated? You mentioned these three categories.

Why has, why did ARM dominate? Why does it dominate the mobile device space? And maybe

my naive intuition is that there are some aspects of power efficiency that are important

that somehow come along with risk. Well, part of it is for these old SIS construction sets,

like in the x86. It was more expensive to these for, you know, they're older. So they have disadvantages

in them because they were designed 40 years ago. But also they have to translate in hardware

from SIS constructions to risk constructions on the fly. And that costs both silicon area that

the chips are bigger to be able to do that. And it uses more power. So ARM has, which has, you

know, followed this risk philosophy is seen to be much more energy efficient. And in today's

computer world, both in the cloud and the cell phone and other things, it isn't the limiting

resource isn't the number of transitions you can fit in the chip. It's what, how much power can you

dissipate for your application. So by having a reduced instruction set, you that's possible to

have a simpler hardware, which is more energy efficient and energy efficiency is incredibly

important in the cloud. When you have tens of thousands of computers in a data center, you want

to have the most energy efficient ones there as well. And of course, for embedded things running

off of batteries, you want those to be our energy efficient and the cell phones too. So I think

it's believed that there's a energy disadvantage of using these more complex instruction set

architectures. So the other aspect of this is if we look at Apple, Qualcomm, Samsung, Huawei,

all use the ARM architecture. And yet the performance of the systems varies. I mean,

I don't know whose opinion you take on, but you know, Apple for some reason seems to perform

perform better in terms of these implementations architecture. So where's the magic enter the

picture? How's that happen? Yeah. So what ARM pioneered was a new business model is they said,

well, here's our proprietary instruction set. And we'll give you two ways to do it.

There will give you one of these implementations written in things like C called Verilog. And

you can just use ours. Well, you have to pay money for that. Not only pay, we'll give you their,

you know, we'll license you to do that, or you could design your own. And so we're talking about

numbers like tens of millions of dollars to have the right to design your own since they

is the instruction set belongs to them. So Apple got one of those the right to build their own.

Most of the other people who build like Android phones just get one of the designs from ARM

to do it themselves. So Apple developed a really good microprocessor design team.

They, you know, acquired a very good team that had was building other microprocessors and brought

them into the company to build their designs. So the instruction sets are the same, the

specifications are the same, but their hardware design is much more efficient than I think everybody

else's. And that's given Apple an advantage in the marketplace and that the iPhones tend to be the

faster than most everybody else's phones that are there.

It'd be nice to be able to jump around and kind of explore different little sides of this.

But let me ask one sort of romanticized question. What do you use the most beautiful aspect or

idea of risk instruction set or instruction sets? Yeah, well, I think, you know, I'm, you know,

I, I was always attracted to the idea of, you know, small is beautiful is that the temptation

in engineering, it's kind of easy to make things more complicated. It's harder to come up with a,

it's more difficult surprisingly to come up with a simple, elegant solution. And I think

that there's a bunch of small features of risk in general that, you know, where you can see this

examples of keeping it simpler makes it more elegant, specifically in risk five, which,

you know, I'm, I was kind of the mentor in the program, but it was really driven by Christos

Sanovic and two grad students, Andrew Waterman, Yensip Lee, is they hit upon this idea of having

a, a subset of instructions, a nice simple structure, subset instructions, like 40 ish

instructions that all software, the software staff risk five can run just on those 40 instructions.

And then they provide optional features that could accelerate the performance instructions

that if you needed them could be very helpful, but you don't need to have them. And that,

that's a new, really a new idea. So risk five has right now, maybe five optional

subsets that you can pull in, but the software runs without them. If you just want to build the,

just the core 40 instructions, that's fine. You can do that. So this is fantastic for

educationally is you can explain computers, you only have to explain 40 instructions

and not thousands of them. Also, if you invent some wild and crazy new technology, like, you know,

biological computing, you'd like a nice simple instruction set. And you can risk five if you

implement those core instructions, you can run, you know, really interesting programs on top of

that. So this idea of a core set of instructions that the software stack runs on, and then optional

features that if you turn them on the compilers were used, but you don't have to, I think is a

powerful idea. What's happened in the past, if for the proprietary instruction sets,

is when they add new instructions, it becomes required piece. And so that all, all microprocessors

in the future have to use those instructions. So it's kind of like, for a lot of people as they

get older, they gain weight, right? That weight in age are correlated. And so you can see these

instruction sets get getting bigger and bigger as they get older. So risk five, you know, lets you

be as slim as you're as a teenager, and you only have to add these extra features, if you're really

going to use them, rather than every, you have no choice, you have to keep growing with the

instruction set. I don't know if the analogy holds up, but that's a beautiful notion that

there's, it's almost like a nudge towards here's the simple core, that's the essential. Yeah, I

think the surprising thing is still, if we, if we brought back, you know, the pioneers from the

1950s and showed them the instruction set architectures, they'd understand it. They'd say, wow,

that doesn't look that different. Well, you know, I'm surprised. And it's, there's, it may be

something, you know, to talk about philosophical things. I mean, there may be something powerful

about those, you know, 40 or 50 instructions that all you need is these commands like these

instructions that we talked about. And that is sufficient to build, to bring about, you know,

artificial intelligence. And so it's a remarkable surprising to me that is complicated as it is

to build these things, you know, a microprocessors where the line widths are narrower than the

wavelength of light, you know, is this amazing technology is at some fundamental level. The

commands that software executes are really pretty straightforward and haven't changed that much in,

in decades, which what a surprising outcome. So underlying all computation, all touring machines,

all artificial intelligence systems, perhaps, might be a very simple instruction set like,

like a risk five or it's, yeah, I mean, that's kind of what I said. I was interested to see,

I had another more senior faculty colleague and he, he had written something in Scientific American

and, you know, his 25 years in the future, and his turned out about when I was a young

professor and he said, yep, I checked it. I was interested to see how that was going to turn out

for me. And it's pretty held up pretty well. But yeah, so there's, there's probably, there's

some, you know, there's, there must be something fundamental about those instructions that were

capable of creating, you know, intelligence from pretty primitive operations and just doing them

really fast. You kind of mentioned a different, maybe radical computational medium, like biological,

and there's other ideas. So there's a lot of spaces in ASIC domain specific. And then there

could be quantum computers and so we can think of all of those different mediums and types of

computation. What's the connection between swapping out different hardware systems and the

instruction set? Do you see those as disjoint or are they fundamentally coupled?

Yeah. So what's, so kind of, if we go back to the history,

you know, when Morris Law is in full effect and you're getting twice as many transistors

every couple of years, you know, kind of the challenge for computer designers is how can

we take advantage of that? How can we turn those transistors into better computers faster,

typically? And so there was an era, I guess in the 80s and 90s where computers were

doubling performance every 18 months. And if you weren't around then, what would happen is

you had your computer and your friend's computer, which was like a year or year and a half newer,

and it was much faster than your computer. And he or she could get their work done

much faster than your time consumers. So people took their computers, perfectly good computers,

and threw them away to buy a newer computer because the computer one or two years later

was so much faster. So that's what the world was like in the 80s and 90s. Well, with the slowing

down of Morris Law, that's no longer true, right? Now with that desktop computers with the laptops,

I only get a new laptop when it breaks, right? Damn, the display broke. I got to buy a new

computer. But before you would throw them away because they were just so sluggish compared

to the latest computers. So that's, you know, that's a huge change of what's gone on.

So but since this lasted for decades, kind of programmers and maybe all of society is used

to computers getting faster regularly. We now believe those of us who are in computer design,

it's called computer architecture, that the path forward is instead is to add accelerators

that only work well for certain applications. So since Morris Law is slowing down, we don't

think general purpose computers are going to get a lot faster. So the Intel processes of the world

are not going to have been getting a lot faster. They've been barely improved me like a few percent

a year. It used to be doubling every 18 months and now it's doubling every 20 years. So it was

so it was just shocking. So to be able to deliver on what Morris Law used to do, we think what's

going to happen. What is happening right now is people adding accelerators to their microprocessors

that only work well for some domains. And by sheer coincidence, at the same time that this

is happening, has been this revolution in artificial intelligence called machine learning.

So with, as I'm sure your other guests have said, AI had these two competing schools of

thought is that we could figure out artificial intelligence by just writing the rules top down

or that was wrong. You had to look at data and infer what the rules are, the machine learning,

and what's happened in the last decade or eight years as machine learning has won.

And it turns out that machine learning, the hardware you build for machine learning is pretty

much multiply. The matrix multiply is a key feature for the way people machine learning is

done. So that's a godsend for computer design. We know how to make matrix multiply run really

fast. So general purpose microprocessors are slowing down. We're adding accelerators for

machine learning that fundamentally are doing matrix multiplies much more efficiently than

general purpose computers have done. So we have to come up with a new way to accelerate things.

The danger of only accelerating one application is how important is that application turns

it turns like machine learning gets used for all kinds of things. So serendipitously we found

something to accelerate that's widely applicable. And we don't even we're in the middle of this

revolution of machine learning. We're not sure what the limits of machine learning are. So this

has been kind of a godsend. If you're going to be able to excel deliver on improved performance,

as long as people are moving their programs to be embracing more machine learning, we know how

to give them more performance even as Moore's law is slowing down. And counterintuitively,

the machine learning mechanism, you can say is domain specific, but because it's leveraging

data, it's actually could be very broad in terms of in terms of the domains that could be applied

in. Yeah, that's exactly right. Sort of it's almost sort of people sometimes talk about the idea

of software 2.0. We're almost taking another step up in the abstraction layer in designing

machine learning systems, because now you're programming in the space of data in the space

in the space of hyper parameters, it's changing fundamentally the nature of programming. And

so the specialized devices that that accelerate the performance, especially neural network based

machine learning systems might become the new general. Yeah. So the this thing that's interesting

point out, these are not coral, these are not tied together. The enthusiasm about machine learning

about creating programs driven from data that we should figure out the answers from data rather

than kind of top down, which classically the way most programming is done in the way artificial

intelligence used to be done. That's a movement that's going on at the same time. Coincidentally,

and the first word machine learning is machines, right? So that's going to increase the demand

for computing. Because instead of programmers being smart writing those those things down,

we're going to instead use computers to examine a lot of data to kind of create the programs.

That's the idea. And remarkably, this gets used for all kinds of things very successfully,

the image recognition, the language translation, the game playing, and you know, it gets into

pieces of the software stack like databases and stuff like that. We're not quite sure

how general purpose it is, but that's going on independent of this hardware stuff.

What's happening on the hardware side is Moore's law is slowing down right when we need a lot more

cycles. It's failing us right when we need it because there's going to be a greater increase

in computing. And then this idea that we're going to do so called domain specific, here's a domain

that your greatest fear is you'll make this one thing work. And that'll help, you know,

5% of the people in the world. Well, this looks like it's a very general purpose thing.

So the timing is fortuitous that if we can, perhaps if we can keep building hardware that

will accelerate machine learning, the neural networks, that'll beat the timing will be right

that that neural network revolution will transform your software, the so called software 2.0.

And the software of the future will be very different from the software of the past. And

just as our microprocessors, even though we're still going to have that same basic risk instructions,

to run a big pieces of the software stack like user interfaces and stuff like that,

we can accelerate the kind of the small piece that's computationally impensive. It's not lots

of lines of code. But it takes a lot of cycles to run that code, that that's going to be the

accelerator piece. And so this that's what makes this from a computer designers perspective,

a really interesting decade. But Hennessy and I talked about in the title of our Turing Warren

speech is a new golden age. We we see this as a very exciting decade, much like when we were

assistant professors, and the risk stuff was going on, that was a very exciting time was where

we were changing what was going on. We see this happening again, tremendous opportunities of people

because we're fundamentally changing how software is built and how we're running it.

So which layer of the abstraction do you think most of the acceleration might be happening?

If you look in the next 10 years, sort of Google is working on a lot of exciting stuff with the

TPU, sort of there's a closer to the hardware that could be optimizations around the around closer

to the instruction set that could be optimization at the compiler level, it could be even at the

higher level software stack. Yeah, it's going to be. I mean, if you think about the old risk

system, it was both it was software hardware was the compiler's improving as well as the

architecture improving and that that's likely to be the way things are now with machine learning,

they they're using domain specific languages, the languages like TensorFlow and PyTorch are

very popular with the machine learning people that those are the raising the level of abstraction

is easier for people to write machine learning in these domain specific languages like like

PyTorch in TensorFlow. So where the most optimization might happen. And so and so there'll be both the

compiler piece and the hardware piece underneath it. So as you kind of the fatal flaw for hardware

people is to create really great hardware but not have brought along the compilers and what

we're seeing right now in the marketplace because of this enthusiasm around hardware for machine

learning is getting, you know, probably billions of dollars invested in startup companies, we're

seeing startup companies go belly up because they focused on the hard work but didn't bring the

software stack along. We talked about benchmarks earlier. So I participated in machine learning

didn't really have a set of benchmarks. I think just two years ago, they didn't have a set of

benchmarks. And we've created something called ML perf, which is machine learning benchmark

suite and pretty much the companies who didn't invest in the software stack couldn't run ML

perf very well. And the ones who did invest in software stack did and we're seeing, you know,

like kind of in computer architecture, this is what happens, you have these arguments about risk

versus this, people spend billions of dollars in the marketplace to see who wins. And it's not,

it's not a perfect comparison, but it kind of sorts things out. And we're seeing companies

go out of business and then companies like, like, there's a company in Israel called Habana.

They came up with machine learning accelerators. They had good ML perf scores. Intel had acquired

a company earlier called Nirvana a couple years ago. They didn't reveal their ML perf scores,

which was suspicious. But a month ago, Intel announced that they're canceling the Nirvana

product line. And they bought Habana for $2 billion. And Intel's going to be shipping Habana

chips, which have hardware and software and run the ML perf programs pretty well. And that's going

to be their product line in the future. Brilliant. So maybe just a link or briefly ML perf. I love

metrics. I love standards that everyone can gather around. What are some interesting aspects of that

portfolio of metrics? Well, one of the interesting metrics is, you know, what we thought it was,

it was, you know, we, I was involved in the start, you know, we, that Peter Mattson is leading the

effort from Google. Google got it off the ground, but we had to reach out to competitors and say,

there's no benchmarks here. This, we think this is bad for the field. It'll be much better if we

look at examples like in the risk days, there was an effort to create a, for the, the people in the

risk community got together, competitors got together, we're building risk microprocessors

to agree on a set of benchmarks that were called spec. And that was good for the industry is rather

before the different risk architectures were arguing, well, you can believe my performance

others, but those other guys are liars. And that didn't do any good. So we agreed on a set of

benchmarks. And then we could figure out who was faster between the various risk architectures,

but it was a little bit faster. But that grew the market rather than, you know, people were

afraid to buy anything. So we argued the same thing would happen with ML perf, you know, companies

like NVIDIA were, you know, maybe worried that it was some kind of trap, but eventually, we all

got together to create a set of benchmarks and do the right thing, right? And we agree on the results

and so we can see whether TPUs or GPUs or CPUs are really faster and how much the faster. And I

think from an engineer's perspective, as long as the results are fair, you're, you can live with it.

Okay, you know, you kind of tip your hat to, to your colleagues at another institution, boy,

they did a better job than us. What you, what you hate is if it's, it's false, right? They're

making claims and it's just marketing bullshit. And, you know, and that's affecting sales.

So you, from an engineer's perspective, as long as it's a fair comparison, and we don't come in

first place, that's too bad, but it's fair. So we wanted to create that environment for ML perf.

And so now there's 10 companies, I mean, 10 universities and 50 companies involved. So pretty

much ML perf has is the, is the way you measure machine learning performance. And, and it didn't

exist even two years ago. One of the cool things that I enjoy about the internet has a few downsides,

but one of the nice things is people can see through BS a little better with the presence of

these kinds of metrics. So it's really nice companies like Google and Facebook and Twitter.

Now it's the cool thing to do is to put your engineers forward and to actually show off

how well you do on these metrics. There's not sort of it, well, there's less of a desire to do

marketing less. So am I, am I sort of naive? No, I think I was trying to understand that,

you know, what's changed from the 80s in this era. I think because of things like social

networking, Twitter and stuff like that, if you, if you put up, you know, a bullshit stuff,

right? That's just, you know, mis, purposely misleading, you know, that you can get a violent

reaction and social media pointing out the flaws in your arguments, right? And so from a marketing

perspective, you have to be careful today that you didn't have to be careful that there'll be people

who, who put out the flaw, you can get the word out about the flaws and what you're saying much

more easily today than in the past. You used to be, it was used to be easier to get away with it.

And the other thing that's been happening in terms of showing off engineers is just

in the software side, people have largely embraced open source software. But it, it was

20 years ago, it was a dirty word at Microsoft. And today Microsoft is one of the big proponents

of open source software. The kind of that's the standard way most software gets built,

which really shows off your engineers, because you can see, if you look at the source code,

you can see who are making the commits, who's making the improvements, who are the engineers

at all these companies who are, are, you know, really great programmers and engineers and making

really solid contributions, which enhances their reputations and the reputation of the companies.

But that's of course not everywhere. Like in the space that I work more in is autonomous vehicles,

and there's still the machinery of hype and marketing still very strong there. And there's

less willingness to be open in this kind of open source way and sort of benchmark. So ML Perf

is represents the machine learning world is much better at being open source about holding itself

to standards of different, the amount of incredible benchmarks in terms of the different computer

vision, natural angle processing tasks is incredible. You know, historically, it wasn't always that way.

I had a graduate student working with me, David Martin. So for in computer in some fields,

benchmarking is been around forever. So computer architecture, databases, maybe operating systems,

benchmarks are the way you measure progress. But he was working with me and then started

working with Jitendra Malik and he's Jitendra Malik and computer vision space. I guess you've

interviewed Jitendra. And David Martin told me, they don't have benchmarks. Everybody has their

own vision algorithm and the way that my, here's my image, look at how well I do. And everybody

had their own image. So David Martin, back when he did his dissertation, figured out a way to do

benchmarks. He had a bunch of graduate students identify images and then ran benchmarks to see

which algorithms run well. And that was, as far as I know, kind of the first time people did

benchmarks and computer vision, which was predated all, you know, the things that eventually led

to ImageNet and stuff like that. But then, you know, the vision community got religion. And then

once we got as far as ImageNet, then that let the guys in Toronto be able to win the ImageNet

competition. And then, you know, that changed the whole world. It's a scary step actually,

because when you enter the world of benchmarks, you actually have to be good to participate,

as opposed to, yeah, you can just, you just believe you're the best in the world.

And I think the people, I think they weren't purposely misleading. I think, if you don't

have benchmarks, I mean, how do you know, you know, you could have your intuition is kind of

like the way we did just do computer architecture. Your intuition is that this is the right

instruction set to do this job. I believe in my experience, my hunch is that's true.

We had to get to make things more quantitative to make progress. And so I just don't know how,

you know, in fields that don't have benchmarks, I don't understand how they figure out how they're

making progress. We're kind of in the vacuum tube days of quantum computing. What are your

thoughts in this wholly different kind of space of architectures? You know, I actually, you know,

quantum computing is ideas been around for a while. And I actually thought, well, I sure hope

I retire before I have to start teaching this. I'd say, because I talk about give these talks

about the slowing of Morris law, and, you know, when we need to change by doing domain specific

accelerators, a common questions say, what about computing? The reason that comes up, it's in the

news all the time. So I think the keep and the third thing to keep in mind is quantum computing

is not right around the corner. There have been two national reports, one by the national

campus of engineering and other by the computing consortium, where they did a frank assessment

of quantum computing. And both of those reports said, you know, as far as we can tell, before you

get error corrected quantum computing, it's a decade away. So I think of it like nuclear fusion,

right? There have been people who've been excited about nuclear fusion a long time. If we ever get

nuclear fusion, it's going to be fantastic for the world. I'm glad people are working on it. But,

you know, it's not right around the corner. Those two reports, to me, say probably it'll

be 2030 before quantum computing is something that could happen. And when it does happen,

you know, this is going to be big science stuff. This is, you know, micro Kelvin,

almost absolute zero things that if they vibrate, if truck goes by, it won't work, right? So this

will be in data center stuff, we're not going to have a quantum cell phone. And it's probably a 2030

kind of thing. So I'm happy that other people are working on it. But just, you know, it's hard

with all the news about it, not to think that it's right around the corner. And that's why we need

to do something as Moore's law is slowing down to provide the computing, keep computing getting

better for this next decade. And, and, you know, we shouldn't be betting on quantum computing,

or expecting quantum computing to deliver in the next few years. It's, it's probably

further off. You know, I'd be happy to be wrong. It'd be great if quantum computing is going to

commercially viable. But it will be a set of applications. It's not a general purpose

computation. So it's going to do some amazing things. But there'll be a lot of things that

probably, you know, the, the old fashioned computers are going to keep doing better for

quite a while. And there'll be a teenager 50 years from now, watching this video saying,

look how silly David Patterson was saying, I said 2030. I didn't say, I didn't say never.

We're not going to have quantum cell phones. So he's going to be watching it. Well, I,

I mean, I, I think this is such a, you know, given we've had Moore's law, I just, I feel

comfortable trying to do projects that are thinking about the next decade. I admire people

who are trying to do things that are 30 years out. But it's such a fast moving field. I just

don't know how to, I'm not good enough to figure out what, what's the problem is going to be in

30 years. You know, 10 years is hard enough for me. So maybe if it's possible to untangle your

intuition a little bit, I spoke with Jim Keller. I don't know if you're familiar with Jim. And he,

he is trying to sort of be a little bit rebellious and to try to think that

he quotes me as being wrong. Yeah. So this, this is the back.

For the record, Jim talks about that he has an intuition that Moore's law is not in fact,

in fact, dead yet. And that it may continue for some time to come.

What are your thoughts about Jim's ideas in this space?

Yeah, this is just, this is just marketing. So, but Gordon Moore said is a quantitative prediction.

We can check the facts, right? Which is doubling the number of transistors every two years. So we

can look back at Intel for the last five years and ask him, let's look at DRAM chips

six years ago. So that would be three two year periods. So then our DRAM chips have eight times

as many transistors as they did six years ago. We can look up Intel microprocessors six years ago.

If Moore's law is continuing, it should have eight times as many transistors

as six years ago. The answer in both those cases is no.

No. The problem has been because Moore's law was kind of genuinely embraced by the semiconductor

industry is they would make investments in similar equipment to make Moore's law come true.

Semiconductor improving and Moore's law in many people's minds are the same thing.

So when I say, and I'm factually correct that Moore's law is no longer holds,

we are not doubling transistors every years years. The downside for a company like Intel is people

think that means it stopped, that technology has no longer improved. And so Jim is trying to

counteract the impression that semiconductors are frozen in 2019 are never going to get better.

So I never said that. All I said was Moore's law is no more. And I'm strictly looking at the

number of transistors because that's what Moore's law is. There's been this aura associated with

Moore's law that they've enjoyed for 50 years about look at the field we're in. We're doubling

transistors every two years. What an amazing field, which is an amazing thing that they were able to

pull off. But even as Gordon Moore said, no exponential can last forever. It lasted for 50

years, which is amazing. And this is a huge impact on the industry because of these changes

that we've been talking about. So he claims, because he's trying to act, he claims Patterson

says Moore's law is no more and look at all, look at it, it's still going. And TSMC, they say it's

no longer, but there's quantitative evidence that Moore's law is not continuing. So what I say now

to try and, okay, I understand the perception problem when I say Moore's law has stopped. Okay,

so now I say Moore's law is slowing down. And I think Jim, which is another way of, if he's,

if it's predicting every two years, and I say it's slowing down, then that's another way of

saying it doesn't hold anymore. And I think Jim wouldn't disagree that it's slowing down.

Because that sounds like it's, things are still getting better and just not as fast,

which is another way of saying Moore's law isn't working anymore. It's still good for marketing.

But what's your, you're not, you don't like expanding the definition of Moore's law sort of

naturally, it's an educator, you know, are, you know, is this like modern politics? Is

everybody get their own facts? Or do we have, you know, Moore's law was a crisp, you know,

a more, it was Carver Mead looked at his, Moore's conversations, drawing on a log log scale,

a straight line. And that's what the definition of Moore's law is. There's this other, what Intel

did for a while, interestingly, before Jim joined them, they said, oh, no, Moore's law isn't the

number of doubling, isn't really doubling transistors every two years. Moore's law is the cost of the

individual transistor going down, cutting in half every two years. Now, that's not what he said,

but they reinterpreted it because they believed that the cost of transistors was continuing

to drop even if they couldn't get twice the main chips. Many people in industry have told me,

that's not true anymore, that basically then the, in more recent technologies that got more

complicated, the actual cost of transistor went up. So even the, a corollary might not be true,

but certainly, you know, Moore's law, that was the beauty of Moore's law. It was a very simple,

it's like he equals MC squared, right? It was like, wow, what an amazing prediction. It's so easy

to understand the implications are amazing. And that's why it was so famous as a prediction. And

this, this reinterpretation of what it meant and changing is, you know, his revisionist history.

And I'd, I'd be happy. And they're not claiming there's a new Moore's law. They're not saying,

by the way, it's, instead of every two years, it's every three years. I don't think they've,

I don't think they want to say that. I think what's going to happen is the new technology

remissions, H1 is going to get a little bit slower. So it is slowing down. The improvements won't be

as great. And that's why we need to do new things. Yeah, I don't like that. The idea of Moore's law

is tied up with marketing. It would be nice if, whether it's marketing or it's, it's,

well, it could be affecting business, but it could also be infecting the imagination of engineers.

Is if, if Intel employees actually believe that we're frozen in 2019, well, that's,

that would be bad for Intel. They, not just Intel, but everybody, it's inspired Moore's law is

inspiring. Yeah, well, everybody, but what's happening right now, talking to people in

who have working in national offices and stuff like that, a lot of the computer science community

is unaware that this is going on, that we are in an era that's going to need radical change at

lower levels that could affect the whole software stack. This, you know, if, if Intel, if you're

using cloud stuff and the servers that you get next year are basically only a little bit faster

than the servers you got this year, you need to know that. And we need to start innovating

to start delivering on it. If you're counting on your software, your software going to add a lot

more features, assuming the computers are going to get faster. That's not true. So are you going to

have to start making your software stack more efficient? Are you going to have to start learning

about machine learning? So it's, you know, it's kind of a, it's a warning or call for arms that

the world is changing right now. And a lot of people, a lot of computer science PhDs are unaware

of that. So a way to try and get their attention is to say that Moore's law is slowing down and

that's going to affect your assumptions. And, you know, we're trying to get the word out. And

when companies like TSMC and Intel say, Oh, no, no, no, Moore's law is fine. Then people think,

Okay, I don't have to change my behavior. I'll just get the next servers. And, you know,

if they start doing measurements, they'll realize what's going on.

It'd be nice to have some transparency and metrics for, for the lay person

to be able to know if computers are getting faster and not to forget. Yeah, there are,

there are a bunch of, most people kind of use clock rate as, as a measure performance, you

know, it's not a perfect one. But if you've noticed clock rates are more or less the same

as they were five years ago, computers are a little better than they aren't, they haven't

made zero progress, but they've made small progress. So you, there's some indications

out there and then our behavior, right? Nobody buys the next laptop because it's so much faster

than the laptop from the past for cell phones. I think I don't know why people buy new cell phones,

because of the new ones announced, the cameras are better, but that's kind of domain specific,

right? They're putting special purpose hardware to make the processing of images go much better.

So that's that that's the way they're doing it. They're not particularly, it's not that the arm

processes in there is twice as fast as much as they've added accelerators to help the experience

of the phone. Can we talk a little bit about one other exciting space, arguably the same level of

impact as your work with risk is RAID. In your, in 1988, you coauthored a paper, a case for redundant

arrays of inexpensive disks, hence RAID RAID. So that's where you introduced the idea of RAID.

Incredible that that little, I mean, little, that paper kind of had this ripple effect and had a

really revolutionary effect. So first, what is RAID? So this is work I did with my colleague,

Randy Katz, and a star graduate student, Garth Gibson. So we had just done the fourth generation

risk project. And Randy Katz, which had an early Apple Macintosh computer. At this time, everything

was done with floppy disks, which are old technologies that could store things that didn't

have much capacity. And you had to, to get any work done, you're always sticking your little

floppy disk in and out, because they didn't have much capacity. But they started building

what are called hard disk drives, which is magnetic material that can remember information

storage for the Mac. And Randy asked the question when he saw this disk next to his Mac, gee,

these are brand new small things. Before that, for the big computers that the disk would be the

size of washing machines. And here's something the size of a kind of the size of a book or so.

Since I wonder what we could do with that. Well, Randy was involved in the fourth generation

risk project here at Berkeley in the 80s. So we figured out a way how to make the computation

part, the processor part go a lot faster. But what about the storage part? Can we do something to

make it faster? So we hit upon the idea of taking a lot of these disks developed for personal

computers and Macintoshes and putting many of them together instead of one of these washing

machine sized things. And so we wrote the first draft of the paper, and we'd have 40 of these

little PC disks, instead of one of these washing machine sized things, and they would be much

cheaper because they're made for PCs. And they could actually kind of be faster because there

was 40 of them rather than one of them. And so we wrote a paper like that and send it to one of

former Berkeley students at IBM. And he says, Well, this is all great and good. But what about

the reliability of these things? Now you have 40 of these devices, each of which are kind of PC

quality. So they're not as good as these IBM washing machines, IBM dominated the storage

genesis. So the reliability is gonna be awful. And so when we calculated it out, instead of,

you know, it breaking on average once a year, it would break every two weeks. So we thought about

the idea and said, Well, we got to address the reliability. So we did it originally

performance, but we had to do reliability. So the name redundant array of inexpensive disks is

array of these disks inexpensive life for PCs, but we have extra copies. So if one breaks,

we won't lose all the information, we'll have enough redundancy that we could let some break and

we can still preserve the information. So the name is an array of inexpensive disks, this is

a collection of these PCs. And the R part of the name was the redundancy, so they'd be reliable.

And it turns out if you put a modest number of extra disks in one of these arrays, it could

actually not only be as faster and cheaper than one of these washing machine disks,

it could be actually more reliable. Because you could have a couple of breaks even with these

cheap disks, whereas one failure with the washing machine thing would knock it out.

Did you, did you have a sense just like with risk that in 30 years that followed,

RAID would take over as a mechanism for storage? I'd say, I think I'm naturally an optimist.

But I thought our ideas were right. I thought kind of like Morris Law, it seemed to me,

if you looked at the history of the disk drives, they went from washing machine size things and

they were getting smaller and smaller. And the volumes were with the smaller disk drives because

that's where the PCs were. So we thought that was a technological trend that disk drives,

the volume of disk drives was going to be getting smaller and smaller devices, which were true.

They were the size of a, I don't know, eight inches diameter, then five inches, then three

inches diameters. And so that it made sense to figure out how to deal things with an array of

disks. So I think it was one of those things where logically, we think the technological

forces were on our side, that it made sense. So we expected it to catch on, but there was that same

kind of business question. IBM was the big pusher of these disk drives in the real world,

where the technical advantage get turned into a business advantage or not. It proved to be true.

And so we thought we were sound technically, and it was unclear whether the business side,

but we kind of, as academics, we believe that technology should win and it did.

And if you look at those 30 years, just from your perspective, are there interesting developments

in the space of storage that have happened in that time? Yeah. The big thing that happened,

well, the couple of things that happened, what we did had a modest amount of storage. So as

redundancy, as people built bigger and bigger storage systems, they've added more redundancy

so they could add more failures. And the biggest thing that happened in storage is,

for decades, it was based on things physically spinning called hard disk drives. We used to

turn on your computer and it would make a noise. What that noise was was the disk drives spinning,

and they were rotating at like 60 revolutions per second. And it's like, if you remember the vinyl

records, if you've ever seen those, that's what it looked like. And there was like

a needle like on a vinyl record that was reading it. So the big drive change is switching that

over to a semiconductor technology called flash. So within the last, I'd say about decade,

is increasing fraction of all the computers in the world are using semiconductor for storage,

the flash drive, instead of being magnetic, their optical, their semiconductor writing of

information into very densely. And that's been a huge difference. So all the cell phones in the

world use flash, most of the laptops use flash, all the embedded devices use flash instead of

storage. Still in the cloud, magnetic disks are more economical than flash, but they use both

in the cloud. So it's been a huge change in the storage industry, this the switching from

primarily disk to being primarily semiconductor. For the individual disk, but still the rate

mechanism applies to those different kinds of disk. Yes, the people will still use rate ideas,

because it's kind of what's different, kind of interesting kind of psychologically, if you

think about it. People have always worried about the reliability of computing since the earliest

days. So kind of, but if we're talking about computation, if your computer makes a mistake,

and the computer says we, the computer has ways to check and say, oh, we screwed up,

we made a mistake. What happens is that program that was running, you have to redo it, which is

a hassle. For storage, if you've sent important information away, and it loses that information,

you go nuts. This is the worst. Oh my God. So if you have a laptop and you're not backing it up

on the cloud or something like this, and your disk drive breaks, which it can do, you'll lose all

that information, and you just go crazy, right? So the importance of reliability for storage is

tremendously higher than the importance of reliability for computation because of the

consequences of it. So yes, so rate ideas are still very popular, even with the switch of the

technology. Although, you know, flash drives are more reliable. You know, if you're not doing anything

like backing it up to get some redundancy, so they handle it, you're taking great risks.

You said that for you, and possibly for many others, teaching and research don't

conflict with each other, as one might suspect. And in fact, they kind of complement each other.

So maybe a question I have is, how has teaching helped you in your research or just in your

entirety as a person who both teaches and does research and just thinks and creates new ideas

in this world? Yes. I think what happens is when you're a college student, you know there's this

kind of tenure system and doing research. So kind of this model that is popular in America,

I think America really made it happen, is we can attract these really great faculty to research

universities, because they get to do research as well as teach. And that especially in fast

moving fields, this means people are up to date and they're teaching those kind of things. So,

but when you run into a really bad professor, a really bad teacher, I think the students think,

well, this guy must be a great researcher, because why else could he be here? So as I, you know,

I, after 40 years at Berkeley, we had a retirement party and I got a chance to reflect and I looked

back at some things. That is not my experience. There's a, I saw a photograph of five of us

in the department who won the Distinguished Teaching Award from campus, a very high honor.

You know, I've got one of those, one of the highest honors. And so there are five of us

on that picture. There's Manuel Blum, Richard Karp, me, Randy Cass and John Osterhout,

contemporaries of mine. I mentioned Randy already. All of us are in the National Academy of Engineering.

We've all run the Distinguished Teaching Award. Blum, Karp and I all have touring awards. Touring

awards, right? You know, the highest award in computing. So that's the opposite, right? It's,

what happens if you, it's, it's, they're highly correlated. So probably the other way to think of

it, if you're very successful people, maybe successful at everything they do, it's not an

either or. And, but it's an interesting question whether specifically, that's probably true, but

specifically for teaching. If there's something in teaching that it's the Richard Feynman, right?

Right. Is there something about teaching that actually makes your research, makes you think

deeper and more outside the box and. Yeah, absolutely. I was going to bring up Feynman.

I mean, he, he criticized the Institute of Advanced Studies. He, so the Institute of Advanced

Studies was this thing that was created near Princeton, where Einstein and all these smart people

went. And when he was invited, he, he thought it was a terrible idea. His, this is a university.

It was supposed to be heaven, right? A university without any teaching,

but he thought it was a mistake is getting up in the classroom and having to explain things to

students and having them ask questions like, well, why is that true? Makes you stop and think. So he

thinks, he thought, and I agree. I think that interaction between a research university and

having students with bright young men asking hard questions at all time is, is synergistic.

And, you know, a university without teaching wouldn't be as vital and exciting a place. And I think

it helps stimulate the, the research. Another romanticized question, but what's your favorite

concept or idea to teach? What inspires you or you see inspire the students? Is there something

to pass to my or, or puts the fear of God in them? I don't know, whichever is most effective.

I mean, in general, I think people are surprised. I've seen a lot of people who don't think they

like teaching, come, come give guest lectures or teach a course and get hooked on seeing the

lights turn on, right? Is people, you can explain something to people that they don't understand.

And suddenly they get something, you know, that's, that's not, that's important and difficult. And

just seeing the lights turn on is a, you know, it's a real satisfaction there. I don't think there's

any specific example of that. It's just the general joy of seeing them, seeing them understand.

I have to talk about this because I've wrestled. I do martial arts. Yeah. Of course, I love wrestling.

I'm a huge, I'm Russian. So I, I had to talk to Dan Gable on podcast. So

I, Dan Gable was my era kind of guy. So you've wrestled at UCLA among many other things you've

done in your life, uh, competitively in sports and science and so on. You've, you've wrestled

maybe again, continue with the romanticized questions, but uh, what have you learned about life?

Yeah. And maybe even science from wrestling or from. Yeah, that's, in fact, um, I wrestled at UCLA,

but also at El Camino Community College. And just right now we were in the state of California,

we were state champions at El Camino. And in fact, I was talking to my mom and, uh, I got into UCLA,

but I decided to go to the community college, which is, it's much harder to go to UCLA than

the community college. And I asked, why did I make the decision? Cause I thought it was because

of my girlfriend. She said, well, it was the girlfriend and, and you thought the wrestling

team was really good. And we were right. We had a great wrestling team. We, we actually,

uh, wrestled against UCLA at a tournament and we beat UCLA as a community college,

which is just freshman and sophomore's. And the part of reason I brought this up is I'm

going to go, they've invited me back at El Camino to give a, uh, a lecture next month.

And so I'm, we've, uh, my friend who was on the wrestling team and, uh, that we're still together,

we're right now reaching out to other members of the wrestling team. We can get together for a

union. But in terms of me, it was a huge difference. I was, I was both, I was kind of, the age cut

off. I was, it was December 1st. And so I was almost always the youngest person in my class.

And I matured later on, you know, our family matured later. So I was almost always the smallest guy.

So, you know, I took, you know, kind of nerdy courses, but I was wrestling. So wrestling was

huge for my, uh, you know, self confidence in high school. And then, you know, I kind of got

bigger at El Camino and in college. And so I had this, uh, kind of physical self confidence.

And it's, and it's translated into research self confidence. Uh, and, uh, and also kind of,

I've had this feeling even today in my seventies, you know, if something,

if something going on in the streets, there's bad physically, I'm not going to ignore it.

Right. I'm going to stand up and try and straighten that out. And that kind of confidence just

carries through the entirety of your life. Yeah. And, and the same things happens intellectually.

If there's something going on where people are saying something that's not true,

I feel it's my job to stand up. And just like I would in the street, if there's something going on,

somebody attacking some woman or something, I'm not, I'm not standing by and letting that get away.

So I feel it's my job to stand up. So it's kind of ironically translates. Uh, the other things

that turned out for both, I had really great college and, uh, high school coaches and they

believed even though wrestling's an individual sport, that would be even more successful as a

team. If we bonded together, you do things that we would support each other rather than everybody,

you know, in wrestling, it's a one on one. And you could be everybody's on their own,

but he felt if we bonded as a team, we'd succeed. So I kind of picked up those skills of how to

form successful teams and how to, from wrestling. And so I think one of, most people would say,

one of my strengths is I can create teams of faculty, large teams of faculty grad students,

pull all together for a common goal and, you know, and, uh, often be successful at it. But I got,

I got both of those things from wrestling. Also, I think I heard this line about if people are in

kind of, you know, collision, you know, sports with physical contact, like wrestling or football

and stuff like that, people are a little bit more, you know, assertive or something. So I think, uh,

I think that also comes through as, you know, and I was, I didn't shy away from the risk,

risk debates, you know, I was, I enjoyed taking on the arguments and stuff like that. So it was,

uh, it was, uh, I'm really glad I did wrestling. I think it was really good for my self image and

I learned a lot from it. So I think that's, you know, sports done well. You know, you, there's

really, uh, lots of positives you can take about it of leadership, um, you know, how to, how to form

teams and how, how to be successful. So we've talked about metrics a lot. There's a really cool, uh,

in terms of bench press and weightlifting, pioneers metric that you've developed that we

don't have time to talk about, but it's, uh, it's a really cool one that people should look into.

It's rethinking the way we think about metrics and weightlifting. But let me talk about metrics

more broadly since that appeals to you in all forms. Let's look at the most ridiculous, the

biggest question of the meaning of life. If you were to try to put metrics on a life well lived,

what would those metrics be? Yeah, a friend of mine, Randy Katz, said this. He said,

you know, when it, when it's time to sign off, it's, it's, uh, the measure isn't the number

of zeros in your bank account. It's the number of inches in the obituary in the New York Times.

Let's see. He said it. I think, you know, having, uh, and you know, this, this is a cliché is that

people don't die wishing they'd spent more time in the office, right? Uh, as I reflect upon my

career, uh, there've been, you know, a half a dozen, a dozen things say I've been proud of.

A lot of them aren't papers or scientific results. Certainly my family, my wife, we've been married,

more than 50 years, uh, kids and grandkids. That's really precious. Uh, education things I've done.

I'm very proud of, uh, you know, books and courses. Uh, I did some help with underrepresented groups

that was effective. So it was interesting to see what were the things I reflected. You know, I had

hundreds of papers, but some of them were the papers, like the risk and rate stuff that I'm proud of,

but a lot of them were, were, were not those things. So people who are just, uh, spend their lives,

you know, going after the dollars or going after all the papers in the world, you know,

that's probably not the things that are afterwards you're going to care about. When I was, uh,

uh, uh, just when I got the offer from Berkeley before I showed up, I read a book where they

interviewed a lot of people and all works of life. And what I got out of that book was the people

who felt good about what they did was the people who affected people as opposed to things that were

more transitory. So I came into this job assuming that it wasn't going to be the papers, it was

going to be relationships with the people over time that, that I would, I would value. And that

was a correct assessment, right? It's, it's the people you work with, the people you can influence,

the people you can help is the things that you feel good about towards your career. It's not,

not the, the stuff that's more transitory. I don't think there's a better way to end it than, uh,

talking about your family, the, the over 50 years of being married to your childhood sweetheart.

Oh, one, one thing I could add is how to, when you tell people you've been married 50 years,

they want to know why. How? Why? I can tell you the nine magic words that you need to say to your

partner to keep a good relationship. And the nine magic words are, I was wrong. You were right.

You were right. I love you. Okay. And you got to say all nine. You can't say, I was wrong. You

were right. You're a jerk. You know, you can't say that. So yeah, I freely acknowledging that you

made a mistake. The other person was right. And that you love them really gets over a lot of bumps

in the road. So that's what I pass along. Beautifully put, David is a huge honor. Thank you so much

for the book you've written for the research you've done for changing the world. Thank you for

talking today. Oh, thanks for the interview. Thanks for listening to this conversation with David

Patterson. And thank you to our sponsors, the Jordan Harbinger show and cash app. Please consider

supporting this podcast by going to jordanharbinger.com slash Lex and downloading cash app and using

code Lex podcast. Click the links by the stuff. It's the best way to support this podcast and

the journey I'm on. If you enjoy this thing, subscribe on YouTube review it with 5,000 podcast

supported on Patreon or connect with me on Twitter. Alex Friedman spelled without the E

try to figure out how to do that is just F R I D M A N. And now let me leave you with some words

from Henry David Thoreau. Our life is faded away by detail. Simplify, simplify.

Thank you for listening and hope to see you next time.