I'm curious to know; is this announcement something that an expert in these sorts of areas could have (or did?) predict months or years ago, given Google's recent jumps forwards in Machine learning products? Can someone with more knowledge about this comment?
Google supercharges machine learning tasks with TPU custom chip
151–160 of 283 posts
Re: Google supercharges machine learning tasks with TPU custom chip
#152Earlier 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/
Well this also shows their commitment to Tensorflow if they willing to pay to fab custom chips for it. Then again, in the Google scheme of things that's probably not a huge cost.
They've probably carefully modeled the energy savings over GPU/FPGA and found it to be substantial enough, even taking into account costs of design changes.
Re: Google supercharges machine learning tasks with TPU custom chip
#153Earlier quoted context omitted.
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.
Yeah, that's why the improvement isn't as big as the 5,000x it was for SHA256 hashing.
Re: Google supercharges machine learning tasks with TPU custom chip
#154Somewhat 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.
Did the image change? I can't seem to find what you're describing.
Re: Google supercharges machine learning tasks with TPU custom chip
#155So now open sourcing of "crown jewels" AI software makes sense. Competitive advantage is protected by custom hardware (and huge proprietary datasets). Everything else can be shared. In fact it is now advantageous to share as much as you can, the bottleneck is a number of people who know how to use new tech.
Re: Google supercharges machine learning tasks with TPU custom chip
#156Somewhat 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.
Did the image change? I can't seem to find what you're describing.
It's still there: the red lines outline the lower left hand corner of the heatsink (the big metallic structure).
Re: Google supercharges machine learning tasks with TPU custom chip
#157Earlier quoted context omitted.
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.
If I recall correctly myself (getting old!), Google has had the ability to build your own custom image since GCE went GA 2.5 years ago. Now, admittadly, it took a while to get IAM and VPC going, but we done did it now!
I'd love to hear what other boring stuff has been a showstopper for you, in case we missed something dumb :)
Re: Google supercharges machine learning tasks with TPU custom chip
#158Earlier quoted context omitted.
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.
If I recall correctly myself (getting old!), Google has had the ability to build your own custom image since GCE went GA 2.5 years ago. Now, admittadly, it took a while to get IAM and VPC going, but we done did it now!
I'd love to hear what other boring stuff has been a showstopper for you, in case we missed something dumb :)
Re: Google supercharges machine learning tasks with TPU custom chip
#159Earlier quoted context omitted.
Did the image change? I can't seem to find what you're describing.
It's still there: the red lines outline the lower left hand corner of the heatsink (the big metallic structure).
Got it. That looks like a bad digital image stitching job. Especially the misaligned fins. But the red lines are odd indeed.
Re: Google supercharges machine learning tasks with TPU custom chip
#160Earlier quoted context omitted.
If you have an application that can tolerate error (like classification), then analog computing can give enormous gains in terms of speed _and_ power efficiency. Essentially, the savings come from using physics to perform the math (see Kirchhoff's current law) vs. using discrete time steps vs. fully-unrolling the logic. Google may not be using analog processing for this version, but I read an analog neural network re…
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 general data converted to images] to be analysed far faster than electronic equivalents.
Going back to the topic of NN-based computing, I found this talk to be intriguing: https://www.youtube.com/watch?v=dkIuIIp6bl0. The main argument is that because Moore's law may no longer be in effect, it will become increasingly important to explore alternate computing solutions. (Google's TPU could be supporting evidence for this argument.) The speaker also co-authored a paper which I liked "General-Purpose Code Acceleration with Limited-Precision Analog Computation".