Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Google supercharges machine learning tasks with TPU custom chip

cloudplatform.googleblog.com

Post reply on HN
Print view View original

201–210 of 283 posts

Re: Google supercharges machine learning tasks with TPU custom chip

#201

Post by engizeer »

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.

All given, the fact that Google itself doesn't extensively use GC is kind of a red flag(I know quite a few Googlers from search infrastructure and none of them said their teams used GCE internally).

A solid guarantee with AWS is if AWS goes down, then a multitude of Amazon's services also will go down(ex Amazonian myself), so it gives me a belief that AWS's uptime is more important to Amazon itself that it is for external customers.

Re: Google supercharges machine learning tasks with TPU custom chip

#202

Post by jldugger »

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…

It makes a lot more sense now why they open sourced Tensor Flow -- they have an ASIC design that supports it, and if they either want to sell the chips, or sell access to them via Google Cloud Platform, they'll need customers. And it's not like HP is about to roll out a server line sporting these any time soon, so Google will continue to have their advantage over the outside world.

Re: Google supercharges machine learning tasks with TPU custom chip

#203

Post by vt240 »

mrpippy wrote: post #120

Bah, SGI made a Tensor Processing Unit XIO card 15 years ago. evidence suggests they were mostly for defense customers: http://forums.nekochan.net/viewtopic.php?t=16728751 http://manx.classiccmp.org/mirror/techpubs.sgi.com/library/m...

I remember going to see the SGI Origin 2000 installation at University of Alaska, Fairbanks. Pretty neat, I seem to remember they had a Cray T3E 900 and some J vector systems there too.

Re: Google supercharges machine learning tasks with TPU custom chip

#204

Post by nickpsecurity »

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.

Don't forget shuttle runs. Adepteva used those and otherwise good engineering practice to develop two products, latest in 65nm, with no more than $2mil. This one might be simpler and cheaper given its requirements.

http://www.adapteva.com/andreas-blog/a-lean-fabless-semicond...

Re: Google supercharges machine learning tasks with TPU custom chip

#205

Post by nickpsecurity »

skaevola wrote: post #82
j1vms wrote: post #58

I wouldn't be surprised if Google is looking to build (or done so already) a highly dense and parallel analog computer with limited precision ADC/DACs. I mean that's simplifying things quite a bit, but it would probably map pretty well to the Tensorflow application.

What are the advantages of analog computing for this application?

Analog circuits run at full-speed (no clock), use little power, and take up little space. An analog computer directly implements the mathematical function it represents as circuits instead of emulating it on a von Neumann-like machine. Long story short, they have issues that made people go digital. Yet, if you can use analog, you can get significant advantages. Example for math acceleration:

http://www.cisl.columbia.edu/grads/gcowan/vlsianalog.pdf

Brain is a bunch of components that are spread out 3D that operate like a mathematical function at slow speed. Mostly sounds analog. Results in us. So, a huge spread of analog components could get some results directly simulating something like that. Here's one of my favorites which is a wafer-scale, analog computer for neural networks.

www.kip.uni-heidelberg.de/Veroeffentlichungen/download.cgi/4713/ps/1856.pdf

Re: Google supercharges machine learning tasks with TPU custom chip

#206

Post by vintermann »

Krakulf wrote: post #111
mike_hearn wrote: post #67

Earlier quoted context omitted.

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/

It might also give Nvidia some sorely needed worries.

Re: Google supercharges machine learning tasks with TPU custom chip

#207

Post by vintermann »

arcanus wrote: post #72
mike_hearn wrote: post #67

Earlier quoted context omitted.

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…

> By revealing that AlphaGo was based on this hardware Interesting, as the nature/science paper made no mention of this, it was exclusively trained on GPUs.

It's possible it was trained on GPUs but ran on TPUs.

Re: Google supercharges machine learning tasks with TPU custom chip

#208

Post by raverbashing »

amelius wrote: post #105
zymhan wrote: post #96

Earlier quoted context omitted.

Wouldn't you typically prototype the design on an FPGA and then manufacture an ASIC once you'd worked out the kinks?

I'm suspecting that the design contains a lot of repetitive units, each of which can easily be tested in software. So perhaps testing on an FPGA isn't even needed.

Ah I'd state the opposite. Your design will have new problems showing up when going from FPGA to ASIC

Especially if you try to make it go faster

Re: Google supercharges machine learning tasks with TPU custom chip

#209

Post by simunaga »

In what sense in this a great news? Yes, it's a progress, so what? After all, you - programmers - earn money for your jobs and pretty soon you might not have one. Because of these kinds of great news -- "Whayyy, this is really interesting, AI, maching learning. Aaaaa!".

"I'll get fired, won't have money for living and AI will take my place, but the world will be better! Yes! Progress!"

Who will benefit from this? Surely not you. Why are you so ecstatic then?

Re: Google supercharges machine learning tasks with TPU custom chip

#210

Post by vintermann »

azinman2 wrote: post #100
agumonkey wrote: post #56

Earlier quoted context omitted.

That was my impression too. ML under the hood was a lot of linear algebra, not very different than most shaders. But maybe Google decided to hardcode a few important ML primitives because the ROI was that good in terms of grabbing customers. Also they might have very large scale applications not found elsewhere that motivates this.

Ok I was obviously oversimplifying things but my point is since we can only speculate, it's clear that when you know specific algorithms/math operations/memory layouts/applications you want to optimize for you can create dedicated chips that optimize and do that quickly. That bitcoin miners are all dedicated chips and run circles around GPUs demonstrates exactly this fact. Furthermore the fact that ML can be error to…

Bitcoin mining is an extremely well-defined task compared to machine learning. It remains to be seen how general these TPUs are in practice - whether they will support the neural network architectures common two years from now.
Post reply on HN