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This is huge. If they really do offer such a perf/watt advantage, they're serious trouble for NVIDIA. Google is one of only a handful of companies with the upfront cash to make a move like this. I hope we can at least see some white papers soon about the architecture--I wonder how programmable it is.
I'm surprised by the perf claims. Nvidia isn't doing kids play. The graph implied they were untouchable in terms of perf...
I think this shows a fundamental difference between Amazon (AWS) and Google Cloud. AWSs offerings seem fairly vanilla and boring. Google are offering more and more really useful stuff: - cloud machine learning - custom hardware - live migration of hosts without downtime - Cold storage with access in seconds - bigquery - dataflow
Meaning even deeper level of vendor lock -- now you cannot even find the chips to run your application elsewhere!
(I work on TF this year.)
IOW, taking their claims at face value, a Nvidia card or Xeon Phi would be expected to smoke one of these, although you might be able to run N of these in the same power envelope.
But those bandwidth & throughput / card limitations would make certain classes of algorithms not really worthwhile to run on these.
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…
What is the capabilities that a piece of hardware like this needs to have to be suitable for machine learning (and not just one specific machine learning problem)?
Maybe they play one move every time someone gets to go there to fix something? or could it be just a way of numbering the racks or something eccentric like that?
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.
Earlier quoted context omitted.
Meaning even deeper level of vendor lock -- now you cannot even find the chips to run your application elsewhere!
No - tensorflow is open source and you can run it on many platforms. TPUs are about efficiency. You might not be able to do image recognition as efficiently without one, but you can still perform exactly the same tasks. (I work on TF this year.)
This is huge. If they really do offer such a perf/watt advantage, they're serious trouble for NVIDIA. Google is one of only a handful of companies with the upfront cash to make a move like this. I hope we can at least see some white papers soon about the architecture--I wonder how programmable it is.
Quantum computers, OpenPower, RISC-V, and now this - I'm really liking Google's recent focus on designing new types of chips and bringing some real competition into the chip market.