Good to hear from you Eric! and thank you for taking a jibe at my questions. Any explanation which extends towards the clarification of my questions is very much appreciated. I am aware that my questions are somewhat indeterminate.
I agree with the neuromorphic systems being the frontrunner when it comes to online learning systems e.g. robotics, autonomous cars, etc. and this is fundamentally due to its hardware architecture. Although, do you (or others) have any numbers on the power consumption comparison of neuromorphic hardware with Google Edge TPUs (or any other device e.g. GPUs/FPGAs)? I am not that focused on the performance comparison with traditional deep nets as from the nengo-dl examples it seem pretty evident that spiking networks have similar accuracies.
Also, do you see an upper edge with neuromophic systems when it comes to implementing cognitive models like motor functions, working memory etc. on them, compared to the TPUs etc.? I guess neuromorphic systems would have an undisputed advantage here too (along with online learning) as their architecture is closer to that of our brain, which could enable us to develop better biologically plausible models?