Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors

arXiv:2303.12167v2 Announce Type: replace-cross
Abstract: Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, as well as unintended parameter and dynamical variations of analog circuits due to fabrication non-idealities. Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2. …

abstract analog applications arxiv asynchronous computation cs.et cs.lg cs.ne deployment devices edge gradient gradient-descent hardware inference low mixed networks neural networks neuromorphic neuromorphic processors parameters power processors robust signal spiking neural networks training type workloads