Ultra-low-power Image Classification on Neuromorphic Hardware

arXiv:2309.16795v2 Announce Type: replace
Abstract: Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to the power consumed when executed on neuromorphic hardware. Training such SNNs using backpropagation through time for vision tasks that rely mainly on spatial features is computationally costly. Training a stateless artificial neural network (ANN) to then convert the weights to an SNN is a straightforward alternative when it comes to image recognition …

abstract applications arxiv backpropagation binary classification cs.cv features hardware image low networks neural networks neuromorphic power replace sparsity spatial spiking neural networks tasks temporal through training type vision