Jeff Dean
speaker
343 appearances
1 recordings
1 series
first heard Aug 2026
last heard 1 Aug
Jeff Dean’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.
Appearances
if you build a specialized chip for low precision dense linear algebra and can't do anything else, that turns out to be really useful for machine learning inference, even though it can't run Chrome or Word or whatever.
And so that system produced a chip a couple years later that was 30 to 80 times more energy efficient than CPUs and GPUs of the day, and also much, much lower latency, like 20 to 30x lower latency.
Yeah, I mean, that's sort of why we built a general purpose linear algebra system, which is what a TPU is really, because we knew ML algorithms were still evolving and you didn't want to over-specialize, but you wanted to specialize enough that you got the dramatic performance benefits of we could have very big multiplier units
We could have high-speed memory, we could have high-speed interconnect, or later TPUs that brought many, many chips to bear on the same problem efficiently.
And we've continued to scale those up and improve their performance over many, many generations now.
Yeah.
I mean, uh, it's always hard to say.
Um, I think, uh,
think about what problems you see and whatever it is you're thinking about, what bottlenecks you see, and
are there very different ways of thinking of the solutions to some of those problems that would get you an order of magnitude or two orders of magnitude better performance or capability or whatever it is?
Because sometimes if you just squint at a problem and you think about not necessarily being anchored on exactly how that problem is solved today, but how you would solve it from first principles, you can come up with really good ideas that are maybe not what other people are thinking about.
Okay, yeah.
Yeah, I mean, I think if you looked at what is important in AI systems these days, you would want to know things like the bandwidth between your main memory system on your accelerator to the on-chip memory to the multiplier unit or whatever.
You want to know how much energy does it take to do a single multiplier operation.
You know, what is the interconnect bandwidth between chips and how much does that, how many chips can you connect with that bandwidth?
And then if you go beyond that domain, like what is the fall off in network bandwidth when you need to talk to 10,000 chips instead of 500 or something?
I think these are all really important numbers to learn.
and really affect how you think about solving particular kinds of problems.
Yeah, just bring it in from HPM on an accelerator into the processor so it can actually compute on it.
Yep.
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