“Transmitting bits of information, even with approaches like bluetooth low energy, is in the tens to hundreds of milliwatts in the best of circumstances at comparatively short range. The efficiency of radio transmission doesn’t seem to be improving dramatically over time either, there seem to be some tough hurdles imposed by physics that make improvements hard.
On a happier note, capturing data through sensors doesn’t suffer from the same problem. There are microphones, accelerometers, and even image sensors that operate well below a milliwatt, even down to tens of microwatts. The same is true for arithmetic. Microprocessors and DSPs are able to process tens or hundreds of millions of calculations for under a milliwatt, even with existing technologies, and much more efficient low-energy accelerators are on the horizon.”
What this means is that most data that’s being captured by sensors in the embedded world is just being discarded, without being analyzed at all.
Scaling machine learning models to embedded devices
UPDATE an example of things moving in that space, startup with chip to move AI to the “edge” https://techcrunch.com/2019/05/20/quadric-io-raises-15m-to-build-a-plug-and-play-supercomputer-for-autonomous-systems/