The power/performance spectrum seems to be very wide:
200uW for 400Kbps throughput with 0.6 accuracy
200W for 400Gps with higher accuracy with 0.99 accuracy
I guess will be interesting where in this range the result (if any) will fall.
200uW for 400Kbps throughput with 0.6 accuracy
200W for 400Gps with higher accuracy with 0.99 accuracy
I guess will be interesting where in this range the result (if any) will fall.
With each situation, your need for precision can vary, and along with it the requirement for processing power.
A model tuned for 75% accuracy is lighter and faster to run (fewer layers) vs 85% (several times bigger, slower). I've made up the numbers to illustrate a point. This is anecdotal and I'm not an ML expert. But here's some evidence:
1. https://ai.googleblog.com/2019/03/an-all-neural-on-device-sp...
2. https://ai.googleblog.com/2018/05/custom-on-device-ml-models...
3. https://krisp.ai/blog/how-we-shrunk-dnn-to-run-inside-chrome...