3 ms·
Yeah I'm confused why e.g. Google's TPUs, etc. didn't catch favour in the market? I'm not an ML developer, so I'm curious. Just economies of scale?
by cmrdporcupine 3y ago
Yeah I'm confused why e.g. Google's TPUs, etc. didn't catch favour in the market?
I'm not an ML developer, so I'm curious. Just economies of scale?
- kimixa 3y agoThere's a natural compromise between "Efficiency" and "Flexibility" - training AIs is currently a fast enough moving field that by the time the really efficient targeted hardware could be released, the state of the art has moved to make them less suited to the latest generation. Outside of training, using the trained network for inference seem to be changing less, so maybe a decent target for bespoke targeted hardware. And indeed it seems that custom hardware blocks are very much "catching favor" in that market - everyone seems to be adding accelerators to their SoCs. GPUs seem to be at a happy medium, where they're often flexible enough to run techniques while still having better efficiency than a CPU.
- xxpor 3y agoWell the first issue is random researchers can't just buy a TPU and throw it in their workstation. (No, corals don't count)