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A significant portion of any real-world AI system is dedicated to extracting relevant features from raw sensor information. Doing that digitally has proven to
by cjhanks 10y ago
A significant portion of any real-world AI system is dedicated to extracting relevant features from raw sensor information. Doing that digitally has proven to have a very high cost rated in both watts and operating temperature.
Most machine learning algorithms are by design resilient to random noise. They can even be learned to be resilient to systematic noise (due to say.. variable hardware performance in mass production).
In those cases; a low power analog device with lower reliability guarantees is 'ok'.
I suspect the "decision engines" (the computation enforcing complex logic based on sensor readings) will probably continue to be on CPU's for some time.
- Retric 10y agoThe problem is you now dramatically increased the cost of training. Which might be ok for speech recognition, but not for self driving cars etc.
- cjhanks 10y agoAny feature extractor worth using will be robust against some amount of Gaussian noise. The neural nets will probably never even see the bad readings. It would surprise me if even unfiltered analog noise in the sensors significantly changed recall. I suppose I don't know off-hand know the magnitude of noise in typical analog systems.