Abstract:
Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SN...Show MoreMetadata
Abstract:
Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.
Published in: 2020 International SoC Design Conference (ISOCC)
Date of Conference: 21-24 October 2020
Date Added to IEEE Xplore: 01 February 2021
ISBN Information:
Print on Demand(PoD) ISSN: 2163-9612