Edge computing is a method of optimizing cloud computing systems "by taking the control of computing applications, data, and services away from some central nodes (the "core") to the other logical extreme (the "edge") of the Internet" which makes contact with the physical world.[1] In this architecture, data comes in from the physical world via various sensors, and actions are taken to change physical state via various forms of output and actuators; by performing analytics and knowledge generation at the edge, communications bandwidth between systems under control and the central data center is reduced. Edge Computing takes advantage of proximity to the physical items of interest also exploiting relationships those items may have to each other.
Funny how computing got centralized, and now is slowly getting decentralized again. I'm happy to see tech for that developing, but I worry that data ownership will continue to be centralized.
Also smaller != more efficient
b) a fully float-trained model "quantized" to int16 typically loses overall precision, but often works well enough. It's also usually faster (if implemented properly).
c) there's a version where you go all the way down to int1 (bits) and binary ops instead of addmuls on floats and ints. It can solve some problems. And properly compiled, it's wicked fast.
There's also a Zen version that uses just 0.5 bits. </joke>
Inference only, no ML training. Only the Cloud has löööörning capabilities.
I bet you can just unscrew the head and flip a dip-switch, and it'll start combining insults in no time.
