Like the direction you are headed. Considering that use of ASICs is going to rise, think you should consider local installs through docker(like machinebox.io) or another technique.
Also federated learning would be next thing to take on.
The ARM core on the SoC is part of the asic. As is the VideoCore part.
Cambricon is going big in china so its not just google and apples. They claim to be 6 times faster than GPU.
I am more interested in potential of being able to run video processing, voice models effortlessly on tiny devices. and also to train models offline or locally.
I think there is a good scope of solutions (like vision recognition) that port well across AI chips.
This is true. Google has TF for tpu and Intel Nervana has neon. Each player will likely publish a software library.