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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

#241

Post by petra »

manav wrote: post #150

Interesting. Plenty of work has been done with FPGAs, and a few have developed ASICs like DaDianNao in China [1]. Google though actually has the resources to deploy them in their datacenters. Microsoft explored something similar to accelerate search with FPGAs [2]. The results show that the Arria 10 (20nm latest from Altera) had about 1/4th the processing ability at 10% of the power usage of the Nvidia Tesla K40 (25w…

>> I wonder if they are using eAsic.

If you read the academic literature, they talk about 100x-3000x energy savings with asic vs GPU. So in that light Google's 10x improvement sounds low, and could certainly fit an eAsic story.

Furthermore, eAsic has fixed wires, but logic is defined via sram configuration(AFAIK) and not fixed, so it could offer a level of programmability .

Re: Google supercharges machine learning tasks with TPU custom chip

#242

Post by dharma1 »

read_only wrote: post #160
dharma1 wrote: post #125

Earlier quoted context omitted.

What do you think about http://optalysys.com/ or http://lighton.io ?

I think Optalysys looks interesting! For the curious, Optalysys has built a general purpose optics-based correlation/pattern matching machine. From some of their predecessor-company marketing material: The correlator performs pattern matching on large data sets such as high-resolution images, providing a measure of similarity and relative position between objects within the input scene. This allows large images [and…

Thank was a fantastic talk, thanks for the link.

Re: Google supercharges machine learning tasks with TPU custom chip

#243

Post by dharma1 »

read_only wrote: post #160
dharma1 wrote: post #125

Earlier quoted context omitted.

What do you think about http://optalysys.com/ or http://lighton.io ?

I think Optalysys looks interesting! For the curious, Optalysys has built a general purpose optics-based correlation/pattern matching machine. From some of their predecessor-company marketing material: The correlator performs pattern matching on large data sets such as high-resolution images, providing a measure of similarity and relative position between objects within the input scene. This allows large images [and…

This was a fantastic talk, thanks for the link.

Re: Google supercharges machine learning tasks with TPU custom chip

#244

Post by grkvlt »

protomok wrote: post #112
kens wrote: post #66

Earlier quoted context omitted.

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.

See also the VAX. It had an assembly instruction to evaluate a polynomial based on a table of coefficients in memory: POLY

http://uranium.vaxpower.org/~isildur/vax/week.html

Re: Google supercharges machine learning tasks with TPU custom chip

#245

Post by jbooth »

boulos wrote: post #172
jbooth wrote: post #115

Earlier quoted context omitted.

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.

"So"? If you're evaluating something today, how does it change your decision that we were late to market with Compute Engine (and in this specific case "bring-your-own-kernel")? If it's about future boring stuff, I think the list of boring stuff isn't too long ;). Disclosure: I work on Compute Engine.

"So" Google lost potential business for a while from people who wanted to spin up VMs rather than wanting to ship code to a proprietary execution framework.

Re: Google supercharges machine learning tasks with TPU custom chip

#246

Post by visarga »

Houshalter wrote: post #234
nathan_f77 wrote: post #233

I'm thinking that this has the potential to change the context of many debates about the "technological singularity", or AI taking over the world. Because it all seems to be based on FUD. While reading this article, one of my first reactions was "holy shit, Google might actually build a general AI with these, and they've probably already been working on it for years". But really, nothing about these chips is unknown…

Just because you understand a machine doesn't mean it can't be dangerous. I could completely understand every aspect of a nuclear bomb, and I could still make a mistake and cause quite a bit of damage with a real one. Complex systems are notorious for having all sorts of unexpected problems, and mistakes happen all the time. How much complex software is entirely bug free? The danger of AI is more than just a random b…

You can condition the AI on the well being and freedom of human population. Hard to define precisely what that means, but it can be approximated with indirect measures. This is just what Asimov thought of in his novels.

Another way to protect against catastrophe would be to launch multiple AI agents that optimize for the goal of nurturing humanity. They can keep each other in check.

Also, humans will evolve as well. Genetics is advancing very fast. We will be able to design bigger/better brains for ourselves, perhaps also with the help of AI. Human learning could be assisted by AIs to achieve much higher levels than today.

We will also be able to link directly to computers and become part of their ecosystem, thus, creating an incentive for it to keep us around. Taking this path would enable uploading and immortality for humans as well.

My guess is that we will all become united with the AI. We already are united by the internet and we spend a lot of time querying the search engine (AI), learning its quirks and, by feedback, helping improving it. This trend will continue up to the point where humans and AI become one thing. Killing humans would be for the AI like cutting out a part of your brain. Maybe it will want a biological brain of its own and come over to the other side, of biological intelligence.

Re: Google supercharges machine learning tasks with TPU custom chip

#247

Post by rolandog »

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…

I wonder if this is a response as well to Nvidia's announced Tesla P100 chip?

Re: Google supercharges machine learning tasks with TPU custom chip

#248

Post by mkj »

bravo22 wrote: post #170

Given the insane mask costs for lower geometries, the ASIC is most likely an Xilinx EasyPath or Altera Hardcopy. Otherwise the amortization of the mask and dev costs -- even for a structured cell ASIC -- over 1K unit wouldn't make much sense versus the extra cooling/power costs for a GPU.

I would suspect they're aiming for orders of magnitudes > 1k units though?

Re: Google supercharges machine learning tasks with TPU custom chip

#249

Post by Symmetry »

LogicFailsMe wrote: post #161
dgacmu wrote: post #149

Earlier quoted context omitted.

Wearing my CMU hat for a moment (but keeping in mind Google's paying me this year): Google's always been cautious about the balance of speed and efficiency, out of concerns about programmer productivity, parallelization, and generality. See, for example, Urs's article in response to my and a few other people's crazy-academic research on using "Wimpy" nodes: http://static.googleusercontent.com/media/research.google.co…

Because there's an absolute sippy straw of bandwidth to the thing if that's a 1x pci-e connection. For if it were delivering performance on par with a $1000 Maxwell class GPU, why wouldn't you guys crow about it? That would be a really big deal wouldn't it? TitanX for 20W? That'd be awesome. And having suffered through multiple pitches for us to buy various FPGA and boutique processors, I have yet to see someone who…

Hmm, I'd expect that once you've got your weights loaded machine learning would need much less bandwidth/flop than graphics does. Is that incorrect?

Re: Google supercharges machine learning tasks with TPU custom chip

#250

Post by jhayward »

RIMR wrote: post #144

Somewhat off topic, but if you look at the lower-left hand corner of the heatsink in the first image, there's two red lines and some sort of image artifact. https://2.bp.blogspot.com/-z1ynWkQlBc8/VzzPToH362I/AAAAAAAAC... They probably didn't mean to use this version of the image for their blog - but I wonder what they were trying to indicate/measure there.

It kind of looks like the original image had a cutaway section there and they didn't want that to be seen so pasted a top surface back on.
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