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Is there anyone who can put into practical terms what these domain-specific processors mean to the future of AI/deep learning?

dharma1 10y ago
Out of the ones mentioned in the article Movidius was/is specifically focused on low power vision tasks, the rest are fundamentally same stuff as we have today with CPUs/GPUs, just faster, lower power. Wave Computing will be interesting if it's not vaporware. I posted some more details about them a few weeks ago - https://news.ycombinator.com/item?id=12458171 https://news.ycombinator.com/item?id=12458171
bmh100 10y ago
A few of the basic concepts: Smarter memory design means that data does not need to be constantly reloaded, saving time that would otherwise be wasted on data transfer. Using lower-precision, fixed-point arithmetic is more efficient than standard-precision floating-point arithmetic. As a result, less time and components are wasted on unnecessary precision. Someone more knowledgeable about processors can better describe the benefits of the Wave architecture. In terms of benchmarks, Wave claims[1] that a single Wave machine is 25x more efficient than 100 GPUs for Google's Inception network. [1]: http://wavecomp.com/technology/ http://wavecomp.com/technology/
- varelse 10y ago
  100 of which GPU? And how were they connected to each other? They almost never give enough details to really understand these bold claims. If this is 100 K40s in a 10 Gb/s datacenter, this is old hat. If this is 100 Titan XPs in Big Sur boxes connected by 100+ Gb/s Infiniband, it's really interesting. I doubt it though. I suspect the only immediate threat to NVIDIA is underestimating AMD GPUs. 2+ years out, who knows?
  - bmh100 10y ago
    Wave seems to be referencing this[1] Google Research blog post. I couldn't find any reference to what GPU cards Google uses. It still provides a basis of comparison to Google Cloud at least. But like you say, a specialized data center deployment probably wouldn't show such a dramatic difference. [1]: https://research.googleblog.com/2016/04/announcing-tensorflow-08-now-with.html https://research.googleblog.com/2016/04/announcing-tensorflo...
emcq 10y ago
Smarter devices. Better voice recognition, smarter cameras, smarter cars, perhaps smarter medical devices. The GPU is hard to beat for training purposes unless you're Google scale, but not acceptable for some of these lower power inference scenarios. You can't reasonably make a product with a GPU today under 10W.

dharma1 10y ago

Out of the ones mentioned in the article Movidius was/is specifically focused on low power vision tasks, the rest are fundamentally same stuff as we have today with CPUs/GPUs, just faster, lower power. Wave Computing will be interesting if it's not vaporware. I posted some more details about them a few weeks ago - https://news.ycombinator.com/item?id=12458171 https://news.ycombinator.com/item?id=12458171

bmh100 10y ago

A few of the basic concepts: Smarter memory design means that data does not need to be constantly reloaded, saving time that would otherwise be wasted on data transfer. Using lower-precision, fixed-point arithmetic is more efficient than standard-precision floating-point arithmetic. As a result, less time and components are wasted on unnecessary precision. Someone more knowledgeable about processors can better describe the benefits of the Wave architecture. In terms of benchmarks, Wave claims[1] that a single Wave machine is 25x more efficient than 100 GPUs for Google's Inception network. [1]: http://wavecomp.com/technology/ http://wavecomp.com/technology/

varelse 10y ago
100 of which GPU? And how were they connected to each other? They almost never give enough details to really understand these bold claims. If this is 100 K40s in a 10 Gb/s datacenter, this is old hat. If this is 100 Titan XPs in Big Sur boxes connected by 100+ Gb/s Infiniband, it's really interesting. I doubt it though. I suspect the only immediate threat to NVIDIA is underestimating AMD GPUs. 2+ years out, who knows?
- bmh100 10y ago
  Wave seems to be referencing this[1] Google Research blog post. I couldn't find any reference to what GPU cards Google uses. It still provides a basis of comparison to Google Cloud at least. But like you say, a specialized data center deployment probably wouldn't show such a dramatic difference. [1]: https://research.googleblog.com/2016/04/announcing-tensorflow-08-now-with.html https://research.googleblog.com/2016/04/announcing-tensorflo...

emcq 10y ago

Smarter devices. Better voice recognition, smarter cameras, smarter cars, perhaps smarter medical devices. The GPU is hard to beat for training purposes unless you're Google scale, but not acceptable for some of these lower power inference scenarios. You can't reasonably make a product with a GPU today under 10W.