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Google supercharges machine learning tasks with TPU custom chip

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Re: Google supercharges machine learning tasks with TPU custom chip

#111

Post by Krakulf »

mike_hearn wrote: post #67
luu wrote: post #63

I'm happy to hear that this is finally public so I can actually talk about the work I did when I was at Google :-). I'm a bit surprised they announced this, though. When I was there, there was this pervasive attitude that if "we" had some kind of advantage over the outside world, we shouldn't talk about it lest other people get the same idea. To be clear, I think that's pretty bad for the world and I really wished th…

Google has always been strategic about announcing what it was doing, even since the early days (I used to work there too). Think about the impact the first MapReduce and GFS/BigTable papers had. My guess as to why they're announcing the TPU is that they are feeling the pressure from Facebook and other AI labs, and want to reinforce their reputation as being the best place to do AI research. By revealing that AlphaGo…

It really boosts Google Cloud Platform with image and prestige, they're coming out with what is pretty much tensorflow as a service with their machine learning product.

Coming out saying they can give you a service no one else can right down to a custom chip may sway a few buyers in the market.

https://cloud.google.com/ml/

Re: Google supercharges machine learning tasks with TPU custom chip

#112

Post by protomok »

kens wrote: post #66
asimuvPR wrote: post #29

Now this is really interesting. I've been asking myself why this hadn't happened before. Its been all software, software, software for the last decade or so. But now I get it. We are at a point in time where it makes sense to adjust the hardware to the software. Funny how things work. It used to be the other way around.

This is known as the Wheel of Reincarnation. Functionality moves to special-purpose hardware then back to software, and the cycle repeats. (The computer term is from 1968 so this has been happening for a long time.) http://www.catb.org/jargon/html/W/wheel-of-reincarnation.htm...

Interesting, I hadn't heard of this term before but it sounds about right!

Maybe the next iteration will be assembly instructions specifically for Neural Nets built into CPUs...actually something like a convolution assembly instruction wouldn't even surprise me at this point.

Re: Google supercharges machine learning tasks with TPU custom chip

#113

Post by blue11 »

luu wrote: post #63

I'm happy to hear that this is finally public so I can actually talk about the work I did when I was at Google :-). I'm a bit surprised they announced this, though. When I was there, there was this pervasive attitude that if "we" had some kind of advantage over the outside world, we shouldn't talk about it lest other people get the same idea. To be clear, I think that's pretty bad for the world and I really wished th…

OK, so what is it? The announcement neither says what a TPU actually is nor what it can do. It's a magic black box. No specs. No price.

Re: Google supercharges machine learning tasks with TPU custom chip

#115

Post by jbooth »

vgt wrote: post #87
gcr wrote: post #80

Earlier quoted context omitted.

Vanilla? Boring? I read "Vanilla" and "Boring" as "Horray, I don't have to spend time rewriting all this complicated code I already have!" If I'm just dipping my toes into (say) Caffe or Theano, I don't have to rewrite it from scratch. That is a huge advantage---not a disadvantage!---of AWS over google.

Your point is valid, but I think what the OP was saying is that Google is offering all this stuff IN ADDITION to the boring stuff. Google does boring stuff very well too.. and one can argue much better than AWS as well.. take a look at Quizlet's story: https://quizlet.com/blog/whats-the-best-cloud-probably-gcp (shamelessly biased Googler)

If I recall correctly, it took Google a while to actually offer the boring stuff. For a while, you could get a Google Compute Engine but you couldn't just get a dang VM image, because Google knows better than you and you should do things their way. They've fixed it now, but lost a lot of potential market share for that conceit.

Re: Google supercharges machine learning tasks with TPU custom chip

#116

Post by protomok »

asimuvPR wrote: post #104
dekhn wrote: post #89

Earlier quoted context omitted.

At some point in the past, Rob Pike mentioned that when we was working on Voyager (that spaceship that almost 40 years after launch, has left the solar system and continues to send back valuable science data), he had a relatively good understanding of the system from the quantum level (transistors are based on quantum theory) to the solar system. He wasn't kidding, either.

Very interesting fact. But the average programmer is not Rob Pike. How do you see this panning out for the average programmer? Will people need to learn a bit about chips to build more efficient CRUD apps?

I think there will always be an API. In the case of Deep Learning you already see Caffe, TensorFlow, etc. readily available for developers.

I don't think the average developer will need to understand chip design but I do think _many_ developers will need to know how to use deep learning frameworks.

Re: Google supercharges machine learning tasks with TPU custom chip

#117

Post by jhartmann »

CydeWeys wrote: post #76
jhartmann wrote: post #7

3 generations ahead of moore law??? I really wonder how they are accomplishing this beyond implementing the kernels in hardware. I suspect they are using specialized memory and an extremely wide architecture. Sounds they also used this for AlphaGo. I wonder how badly we were off on AlphaGo's power estimates. Seems everyone assumed they were using GPU's, sounds like they were not. At least partially. I would really LO…

It seems entirely reasonable to me, and there's good historical precedent for it. Let's look at SHA256 hashing as an example. The maximum number of hashes that the best GPU around can do is around 1 GHash/s. However, for the same cost, of around $600, specialized hardware can do around 5 THash/s. That's about five thousand times the performance/price. There's no reason that hardware that is super-specific to neural n…

Understood, but GPU's are very good at linear algebra. GPU's are HORRIBLE for crypto, just very parallel.

Re: Google supercharges machine learning tasks with TPU custom chip

#118

Post by taliesinb »

I don't know much about this sort of thing but I wonder if the ultimate performance would come with co-locating specialized compute with memory, so that the spatial layout of the computation on silicon ends up mirroring the abstract dataflow dag, with fairly low-bandwidth and energy efficient links between static register arrays that represent individual weight and grad tensors. Minimize the need for caches and power hungry high bandwidth lanes, ideally the only data moving around is your minibatch data going one way and your grads going the other way.

I wonder if they're doing that, and to what degree.

Re: Google supercharges machine learning tasks with TPU custom chip

#119

Post by omarforgotpwd »

retox wrote: post #88
babo wrote: post #79

Earlier quoted context omitted.

Sure, but that's the deal. I'll buy the latest nVidia 1080 card as soon as I can but renting these custom chips per minute would be a way better option for me.

GPUs also have this nice side effect of being great at playing games on. Purely as a guess I'd think that the gaming market is bigger than the AI researcher market.

In a future where AI is everywhere, Nvidia hopes it can sell GPUs by the hundreds and thousands to large data centers. You can make a lot more money a lot faster selling your hardware this way, and Nvidia is very interested in it judging from how much they talked about it at their recent conference.
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