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Google's machine learning oriented chips have gotten an upgrade:
At Google I/O 2017, Google announced its next-generation machine learning chip, called the "Cloud TPU." The new TPU no longer does only inference--now it can also train neural networks.
[...] In last month's paper, Google hinted that a next-generation TPU could be significantly faster if certain modifications were made. The Cloud TPU seems to have have received some of those improvements. It's now much faster, and it can also do floating-point computation, which means it's suitable for training neural networks, too.
According to Google, the chip can achieve 180 teraflops of floating-point performance, which is six times more than Nvidia's latest Tesla V100 accelerator for FP16 half-precision computation. Even when compared against Nvidia's "Tensor Core" performance, the Cloud TPU is still 50% faster.
[...] Google will also donate access to 1,000 Cloud TPUs to top researchers under the TensorFlow Research Cloud program to see what people do with them.
Previously: Google Reveals Homegrown "TPU" For Machine Learning
Google Pulls Back the Covers on Its First Machine Learning Chip
Nvidia Compares Google's TPUs to the Tesla P40
NVIDIA's Volta Architecture Unveiled: GV100 and Tesla V100
(Score: 0) by Anonymous Coward on Friday May 19 2017, @01:17AM (1 child)
> takyon [soylentnews.org] writes:
I've been wondering, is it:
+ ta - ka - on
+ tak - yon
+ tacky - on
or some other pronunciation?
(Score: 2) by takyon on Friday May 19 2017, @01:25AM
It's a little like this. [youtube.com]
[SIG] 10/28/2017: Soylent Upgrade v14 [soylentnews.org]
(Score: 2) by MichaelDavidCrawford on Friday May 19 2017, @01:33AM (3 children)
The TPU is smarter than our Commander-in-Chief.
Yes I Have No Bananas. [gofundme.com]
(Score: 3, Interesting) by takyon on Friday May 19 2017, @01:37AM (2 children)
TPUs are ineligible to become the President of the United States.
Here's a short story idea for mcgrew: Donald Trump becomes the first human to be mind uploaded, and the resulting AI quickly becomes smarter than any other human while discovering profound insights about its copied former experiences.
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(Score: 0) by Anonymous Coward on Friday May 19 2017, @05:01AM
Well in that case, my vote goes to the inanimate carbon rod.
(Score: 2) by DeathMonkey on Friday May 19 2017, @05:53PM
"Holy crap I was literally wrong about everything." THE END
(Score: 2) by takyon on Friday May 19 2017, @01:52AM (4 children)
That's right, GV100/Tesla V100 is rated for 120 TFLOPS of "tensor operations".
So how much does a TPU cost?
https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu [google.com]
Still not enough info, but they might be onto something.
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(Score: 2) by takyon on Friday May 19 2017, @02:09AM (3 children)
The world's fastest supercomputer, Sunway TaihuLight, has 40,960 "Chinese-designed SW26010 manycore 64-bit RISC processors based on the Sunway architecture". Speed is 105 petaflops, 125 petaflops peak (LINPACK, so take it with some salt).
I believe the "Cloud TPU" is 4 smaller TPUs in one unit (not sure). So tensor performance per individual TPU is 45 (tensor) "teraflops". So you get these numbers [nextbigfuture.com]:
It seems to scale well. Anyway, to reach 125 petaflops you would need 2,778 of them, and to get to 1 exaflops, 22,222. It would probably cost well under a billion dollars for Google to build the machine learning equivalent of an exaflop.
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(Score: 2) by kaszz on Friday May 19 2017, @04:32AM (2 children)
36.8e15 FLOPS is the estimated computational power required to simulate a human brain in real time..
At a price of 30 million dollars?
(Score: 2) by HiThere on Friday May 19 2017, @05:10PM (1 child)
It depends on which estimate you use. We don't even have nearly an order of magnitude of that number. Particularly if you allow the exclusion of parts of the brain that are dedicated to, e.g., handling blood chemistry. And particularly if you include speculation that some quantum effects happen in thought.
In fact, the entire basis of thought isn't really understood, so flops might be a poor way to simulate it. Perhaps integer arithmetic is better. Or fixed point. That flops are important is due to the selected algorithm, and I'm really dubious about it. That said, this doesn't imply that the current "deep learning" approach won't work. It's just that you can't assume that its computational requirements will be equivalent. They could also be either much higher or much lower.
Javascript is what you use to allow unknown third parties to run software you have no idea about on your computer.
(Score: 2) by kaszz on Friday May 19 2017, @05:27PM
Well now that the capacity becomes available. Maybe it will enable research to find out?
(Score: 2) by kaszz on Friday May 19 2017, @04:26AM (4 children)
It seems how the chip works is pretty well known. So when will the first open source chip show up?
Any researcher that uses these online TPUs can be sure that google gobles it up. And any productivity, patent, value addition goes to share holders etc.
A open source solution will enable more free development at a initial performance penalty. In the meantime FPGAs can serve as a test platform?
(Score: 0) by Anonymous Coward on Friday May 19 2017, @09:46AM (1 child)
Is some patented technology involved?
(Score: 2) by kaszz on Friday May 19 2017, @04:50PM
Do it in China and get back through mailbag with "electric stuff" ?
(Score: 2) by LoRdTAW on Friday May 19 2017, @04:38PM (1 child)
I have a feeling that it won't happen. TPU's might never see the light of day outside of a Google data center because google would lose control of the AI race. We may see open source tools to talk to said AI systems but the platform itself will be a proprietary black box.
We are moving rapidly towards a closed computing world where the likes of Google, Amazon, and others seek to aggregate all of your computing needs into THEIR data centers. The idea being that everyone has to pay a recurring fee or are duped into freely working for said companies by letting them commoditize data mined from every corner of your digital life and sell it. You will also be duped into doing the dirty footwork for gathering other data such as poking through your videos, photos, locations and other data.
All we will be left with are locked down dumb terminals in the form of tablets, "smart" TV's and phones. Desktop/Laptop computers will eventually be abandoned by said companies to "focus on delivering a user friendly multimedia platform" . They will be about as modern and stylish as 1970's decor. The walled gardens which are for now avoidable might one day be the only choice. I once thought it was paranoia to think like this but now it is becoming more and more real at a faster and faster pace. VR, AR, AI, all every other buzz acronym will all have us enslaved one day. But not to a giant computer but the almighty dollar, the man behind the curtain.
(Score: 2) by kaszz on Friday May 19 2017, @04:47PM
It will be the data center behind the fiber ;-)
The component to make free is the process to make chips. Once that is accomplished their monopoly significantly decreases.
(Score: 2) by jasassin on Friday May 19 2017, @04:48AM (1 child)
Yeah, but can it run Grand Theft Auto 5 at 4K over 60FPS?
jasassin@gmail.com GPG Key ID: 0xE6462C68A9A3DB5A
(Score: 2) by kaszz on Friday May 19 2017, @06:04AM
Maybe if you can wire a really fast bus between a board with TPUs and the graphics card. Probably using the SLI port or PCI-e data pair. Then it's just the task to write some code..
(Score: 1, Informative) by Anonymous Coward on Friday May 19 2017, @11:40AM (1 child)
TL;DR TPU = google's new funky spy processor
Tensor processing units (or TPUs) are application-specific integrated circuits (ASICs) developed specifically for machine learning. Compared to graphics processing units, they are designed explicitly for a higher volume of reduced precision computation (e.g. as little as 8-bit precision) with higher IOPS per watt, and lack hardware for rasterisation/texture mapping. The chip has been specifically designed for Google's TensorFlow framework, however Google still uses CPUs and GPUs for other types of machine learning. Other AI accelerator designs are appearing from other vendors also and are aimed at embedded and robotics markets. -- https://en.wikipedia.org/wiki/Tensor_processing_unit [wikipedia.org]
(shit like this would be good in the summary BTW HTH LOL)
(Score: 0) by Anonymous Coward on Friday May 19 2017, @12:19PM
> ... Compared to graphics processing units, they are designed explicitly for a higher volume of reduced precision computation (e.g. as little as 8-bit precision) ...
When you say it like that, it almost sounds like ML could be viewed as an extension of Fuzzy Logic, or at least use Fuzzy as an analogy? (sorry, don't have any car analogies today)
My take (back then) was that the Fuzzy proponents took a very low precision approach to control systems--but higher precision than the most simple controllers like a bang-bang thermostat. Instead of all that boring system identification and modeling of the plant in classical/analog control theory, Fuzzy promised quick 'n dirty stable controllers that worked "well enough" for some applications.