SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems

arXiv:2402.11322v1 Announce Type: cross
Abstract: Spiking Neural Networks (SNNs) offer a promising solution to achieve ultra low-power/energy computation for solving machine learning tasks. Currently, most of the SNN architectures are derived from Artificial Neural Networks whose neurons' architectures and operations are different from SNNs, or developed without considering memory budgets from the underlying processing hardware. These limitations hinder the SNNs from reaching their full potential in accuracy and efficiency. Towards this, we propose SpikeNAS, a novel memory-aware neural architecture search …

abstract architecture architectures artificial artificial neural networks arxiv computation cs.ai cs.lg cs.ne energy framework low machine machine learning memory network networks neural architecture search neural network neural networks neurons operations power search snn solution spiking neural network spiking neural networks systems tasks type