> They say it accelerates TensorFlow by 10x
They say 10x performance / watt, nothing about performance per unit time.
They say 10x performance / watt, nothing about performance per unit time.
The speed at which an ASIC will run is constrained by temperature (power dissipation) and and logic timing, which itself has a dependency on temperature.
So we could call that vertical scaling, to some power ceiling which may not take us all the way to 10x, but it's not impossible.
Then there is horizontal, which I assume is applicable to these problems... running more in parallel.
In both cases, I think it's safe to assume they are getting a performance increase in the instantaneous sense.
While I agree some performance per unit increase is likely, how does a direct 10x increased based on power savings follow? Less power usage does not mean that the chip can run through more flops in the same amount of time, right?
http://electronics.stackexchange.com/questions/122050/what-l...
(see graph in the first answer)
Also, it's not known that the TPU have a way to allow to increase the clockspeed arbitrarily, nor is it known whether their architecture is capable of ensuring correctness at arbitrary clock frequencies. Some architectures make assumptions like "The time for this gate to reach saturation is very small compared to the clock frequency, so we'll pretend that it's instantaneous."
Well, that's the kind of metric you'd expect from a cloud provider. That's what's important to them.
If you're a tinkerer dabbling in TPU acceleration on your gaming/coding PC alongside with GPU acceleration, then the metric that would be interesting for you is speed increase per unit.