Forward Direct Feedback Alignment for Online Gradient Estimates of Spiking Neural Networks

arXiv:2403.08804v1 Announce Type: cross
Abstract: There is an interest in finding energy efficient alternatives to current state of the art neural network training algorithms. Spiking neural network are a promising approach, because they can be simulated energy efficiently on neuromorphic hardware platforms. However, these platforms come with limitations on the design of the training algorithm. Most importantly, backpropagation cannot be implemented on those. We propose a novel neuromorphic algorithm, the \textit{Spiking Forward Direct Feedback Alignment} (SFDFA) algorithm, an adaption of …

abstract algorithms alignment art arxiv cs.lg cs.ne current energy energy efficient feedback gradient hardware however limitations network networks network training neural network neural networks neuromorphic platforms spiking neural network spiking neural networks state state of the art training type