Jim Keller is a legendary microprocessor engineer, having worked at AMD, Apple, Tesla, and now Intel. He's known for his work on the AMD K7, K8, K12 and Zen microarchitectures, Apple A4, A5 processors, and co-author of the specifications for the x86-64 instruction set and HyperTransport interconnect.

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OUTLINE:
0:00 - Introduction
2:12 - Difference between a computer and a human brain
3:43 - Computer abstraction layers and parallelism
17:53 - If you run a program multiple times, do you always get the same answer?
20:43 - Building computers and teams of people
22:41 - Start from scratch every 5 years
30:05 - Moore's law is not dead
55:47 - Is superintelligence the next layer of abstraction?
1:00:02 - Is the universe a computer?
1:03:00 - Ray Kurzweil and exponential improvement in technology
1:04:33 - Elon Musk and Tesla Autopilot
1:20:51 - Lessons from working with Elon Musk
1:28:33 - Existential threats from AI
1:32:38 - Happiness and the meaning of life

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The following is a conversation with Jim Keller, legendary microprocessor engineer who has worked at AMD, Apple, Tesla, and now Intel.

He's known for his work on AMD K7, K8, K12, and Zen microarchitectures, Apple A4 and A5 processors, and co-author of the specification for the x86-64 instruction set and HyperTransport Interconnect.

He's a brilliant first principles engineer and out of the box thinker, and just an interesting and fun human being to talk to.

This is the Artificial Intelligence Podcast.

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And now here's my conversation with Jim Keller.

the differences and similarities between the human brain and a computer with a microprocessor at its core?

Let's start with the philosophical question, perhaps.

Well, since people don't actually understand how human brains work, I think that's true.

Computers are, you know, there's really 2 things.

There's memory and there's computation, right?

And to date, almost all computer architectures are global memory, which is a thing, right?

And then computational, where you pull data in, you do relatively simple operations on it, and write data back.

So it's decoupled in modern computers, and you think in the human brain, everything's a mesh, a mess that's combined together?

What people observe is there's some number of layers of neurons which have local and global connections and information is stored in some distributed fashion.

And people build things called neural networks in computers where the information is distributed in some kind of fashion.

You know, there's a mathematics behind it.

I don't know that the understanding of that is super deep.

The computations we run on those are straightforward computations.

I don't believe anybody has said a neuron does this computation.

So, to date it's hard to compare them, I would say.

So let's get into the basics before we zoom back out.

How do you build a computer from scratch?

Maybe even as far back as what is a transistor?

So the special charm of computer engineering is there's a relatively good understanding of abstraction layers.

So down at the bottom you have atoms and atoms get put together in materials like silicon or dope silicon or metal and we build transistors.

On top of that, we build logic gates, right?

And then functional units, like an adder, a subtractor, an instruction parsing unit, and then we assemble those into processing elements.

Modern computers are built out of probably 10 to 20 locally organic processing elements or coherent processing elements, and then that runs computer programs.

And then software, there's an instruction set you run.

And then there's assembly language C, C++, Java, JavaScript, there's abstraction layers, essentially from the atom to the data center, right?

So when you build a computer, first there's a target, like what's it for?

Which today there's a whole bunch of metrics about what that is.

And then in an organization of 1,000 people who build a computer, there's lots of different disciplines that you have to operate on.

So there's a bunch of levels of abstraction in organization like Intel and in your own vision, there's a lot of brilliance that comes in at every 1 of those layers.

Some of it is science, some of it is engineering, some of it is art.

What's the most, if you could pick favorites, what's the most important, your favorite layer on these layers of abstractions?

Where does the magic enter this hierarchy?

That's the fun, you know, I'm somewhat agnostic to that.

So I would say for relatively long periods of time, instruction sets are stable.

So the x86 instruction set, the ARM instruction set.

So it says, how do you encode the basic operations?

Load, store, multiply, add, subtract, conditional branch.

There aren't that many interesting instructions.

If you look at a program and it runs, 90% of the execution is on 25 opcodes, 25 instructions.

Intel architecture has been around for 25 years.

And that's because the basics are defined a long time ago.

Now, the way an old computer ran is you fetched instructions and you executed them in order.

The way a modern computer works is you fetch large numbers of instructions, say

And then you find the dependency graph between the instructions.

And then you execute in independent units those little micrographs.

So a modern computer, like people like to say, computers should be simple and clean, but it turns out the market for simple, clean, slow computers is 0, right?

We don't sell any simple, clean computers.

Now you can, there's how you build it can be clean, but the computer people want to buy, that's say in a phone or a data center, fetches a large number of instructions, computes the dependency graph, and then executes it in a way that gets the right answers.

Yeah, they run deeply out of order and then there's semantics around how memory ordering works and other things work.

So the computer sort of has a bunch of bookkeeping tables that says, what order do these operations finish in or appear to finish in?

But to go fast, you have to fetch a lot of instructions and find all the parallelism.

Now there's a second kind of computer, which we call GPUs today.

And I call it the difference, There's found parallelism, like you have a program with a lot of dependent instructions, you fetch a bunch, and then you go figure out the dependency graph, and you issue instructions out of order.

That's because you have 1 serial narrative to execute, which in fact can be done out of order.

So yeah, so humans think in serial narrative.

There's a sentence after sentence after sentence, and there's paragraphs.

Imagine you diagrammed it properly and you said, which sentences could be read in any order, any order without changing the meaning?

So some paragraphs could be reordered, some sentences can be reordered.

You could say, he is tall and smart and X, right?

And it doesn't matter the order of tall and smart.

But if you say the tall man is wearing a red shirt, what colors, you know, like you can create dependencies, right?

And so GPUs, on the other hand, run simple programs on pixels.

But you're given a million of them and the first order, the screen you're looking at doesn't care which order you do it in.

Simple narratives around the large numbers of things where you can just say it's parallel because you told me it was.

So found parallelism where the narrative is sequential but you discover little pockets of parallelism versus?

So once you crack the problem, you say, here's how you fetch 10 instructions at a time, here's how you calculate the dependencies between them, here's how you describe the dependencies, here's, you know, these are pieces, right?

So, once you describe the dependencies, then it's just a graph, sort of, it's an algorithm that finds, what is that?

I'm sure there's a graph theory, theoretical answer here that's solvable.

In general, programs, modern programs that human beings write, how much found parallelism is there in them?

You would get what's called cycles per instruction and it would be about, you know, 3 instructions, 3 cycles per instruction because of the latency of the operations and stuff.

And in a modern computer, excuse it, but like

1 is the found parallelism in the narrative, right?

And the other is to predictability of the narrative.

So certain operations, they do a bunch of calculations and if greater than 1, do this, else do that.

That decision is predicted in modern computers to high 90% accuracy.

So imagine you have a decision to make every 6 instructions, which is about the average, right?

But you want to fetch 500 instructions, figure out the graph and execute them all in parallel.

That means you have, let's say, if you fix 600 instructions and it's every 6, you have to fetch, you have to predict 99 out of a hundred branches correctly for that window to be effective.

Okay, so parallelism, you can't parallelize branches.

So imagine you do a computation over and over.

And you go through that loop a million times.

So every time you look at the branch, you say it's probably still greater than 1.

And you're saying you could do that accurately.

years ago, you simply recorded which way the branch went last time and predicted the same thing.

So then somebody said, hey, let's keep a couple of bits and have a little counter so when it predicts 1 way, we count up and then pins.

So say you have a three-bit counter, so you count up and then you count down.

And if it's, you know, you can use the top bit as the sign bit, so you have a sign 2 bit number.

So if it's greater than 1, you predict taken and less than 1, you predict not taken, right?

This branch depends on how you got there.

So if you came down the code 1 way, you're talking about Bob and Jane, right?

It went 1 way, but if you're talking about Bob and Jill, does Bob like Jane, you go a different way, right?

That's cool, but that's not how anything works today.

They use something that looks a little like a neural network.

So modern, you take all the execution flows and then you do basically deep pattern recognition of how the program is executing.

And you do that multiple different ways, and you have something that chooses what the best result is.

There's a little supercomputer inside the computer.

So the effective window that is worth finding grass in gets bigger.

So this is 1 of those, to get the result, to get from a window of say 50 instructions to 500, It took 3 orders of magnitude or 4 orders of magnitude more bits.

Now if you get the prediction of a branch wrong, what happens then?

You flush the pipe, so it's just the performance cost.

So we're starting to look at stuff that says, so they executed down this path, and then you had 2 ways to go, but far, far away, there's something that doesn't matter which path you went.

So you took the wrong path, you executed a bunch of stuff.

Then you had the mispredicting, you backed it up, but you remembered all the results you already calculated.

Like if you read a book and you misunderstand a paragraph, you're understanding the next paragraph, sometimes it's invariant to their understanding.

And you can kind of anticipate that invariance.

Yeah, well, you can keep track of whether the data changed.

And so when you come back to a piece of code, should you calculate it again or do the same thing?

Okay, how much of this is art and how much of it is science?

So imagine you come to a point in the road where you have to make a decision.

And you have a bunch of knowledge about which way to go.

So you want to go the shortest way, or do you want to go the fastest way, or do you want to take the nicest road?

So imagine you're doing something complicated like building a computer, and there's hundreds of decision points all with hundreds of possible ways to go.

And the ways you pick interact in a complicated way.

And then you have to pick the right spot.

You avoided the question, you just described the Robert Frost problem of road less taken.

Yeah, I don't know how to describe that because some people are very good at making those intuitive leaps.

It seems like there's combinations of things.

Some people are less good at it, but they're really good at evaluating the alternatives.

Right, and everybody has a different way to do it.

And some people can't make those leaps, but they're really good at analyzing it.

So when you see Computers are designed by teams of people who have very different skill sets.

And a good team has lots of different kinds of people.

I suspect you would describe some of them as artistic.

And the 1% inspiration is really important.

And then there are interesting things to do at every level of that stack.

At the end of the day, if you run the same program multiple times, does it always produce the same result?

So if you run a correct C program, the definition is every time you run it, you get the same answer.

Well That's a language definitional statement.

So for years when people did, when we first did 3D acceleration of graphics, you could run the same scene multiple times and get different answers.

And then some people thought that was okay, and some people thought it was a bad idea.

And then when the HPC world used GPUs for calculations, they thought it was a really bad idea.

Now, in modern AI stuff, people are looking at networks where the precision of the data is low enough that the data is somewhat noisy.

And the observation is the input data is unbelievably noisy.

So why should the calculation be not noisy?

And people have experimented with algorithms that say can get faster answers by being noisy.

Like as a network starts to converge, if you look at the computation graph, it starts out really wide and then it gets narrower.

And you can say, is that last little bit that important?

Or should I start the graph on the next rev before we whittle it all the way down to the answer, right?

So you can create algorithms that are noisy.

Now if you're developing something and every time you run it you get a different answer, it's really annoying.

And so most people think even today, every time you run the program you get the same answer.

Now I know, but the question is, that's the formal definition of a programming language.

There is a definition of languages that don't get the same answer, but people who use those.

You always want something because you get a bad answer and then you're wondering is it because of something in the algorithm or because of this?

And so everybody wants a little switch that says no matter what, do it deterministically.

And it's really weird because almost everything going into modern calculations is noisy, so why do the answers have to be so clear?

I design computers for people who run programs.

So if somebody says, I want a deterministic answer, like most people want that.

Can you deliver a deterministic answer, I guess is the question, like when you.

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Jim Keller: Moore's Law, Microprocessors, and First Principles | Lex Fridman Podcast #70