I came across this interesting article and it prompted few questions to me. Sorry for these probably vague ones, but I was curious to know more from the experts here.
1> Compared to the current TPUs, NPUs, and VPUs, how would you rank the current state-of-the-art neuromorphic hardware in terms of deep learning performance and power consumption? From the article it seems that the Google’s Edge TPUs are at the forefront.
2> The deep learning success of the neuromorphic hardware probably depends heavily on the advancements in the spiking research. Assuming that sufficient required advancements have been made in spiking research (similar to the present maturity in the current traditional deep nets), is there a possibility of neuromorphic hardware replacing the TPUs, NPUs, VPUs, or any other architectures in future? I completely understand that the replacement is not going to be in its entirety but what could be the extent of it?
3> If the answer to the above question is Yes, then how long do you think it will take for the neuromorphic systems to penetrate the technological industry to the same level (and beyond) as todays GPUs/TPUs/FPGAs have done?
4> Edit: Are there domains other than deep learning where neuromorphic systems would be the de facto? I guess for cognitive modelling?
Thank you for your time!