I guess they want silicon for different uses. Embedded with low power consumption (on a soldier/handheld for example), in a vehicle, in a datacenter, etc. With

I guess they want silicon for different uses. Embedded with low power consumption (on a soldier/handheld for example), in a vehicle, in a datacenter, etc. 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-speech.html?m=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.html?m=1 https://ai.googleblog.com/2018/05/custom-on-device-ml-models... 3. https://krisp.ai/blog/how-we-shrunk-dnn-to-run-inside-chrome/ https://krisp.ai/blog/how-we-shrunk-dnn-to-run-inside-chrome...