AUTHOR=Habara Takehiro , Sato Takashi , Awano Hiromitsu TITLE=BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction JOURNAL=Frontiers in Neuroscience VOLUME=18 YEAR=2024 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1420119 DOI=10.3389/fnins.2024.1420119 ISSN=1662-453X ABSTRACT=

Spiking neural networks (SNNs) have garnered significant attention due to their notable energy efficiency. However, conventional SNNs rely on spike firing frequency to encode information, necessitating a fixed sampling time and leaving room for further optimization. This study presents a novel approach to reduce sampling time and conserve energy by extracting early prediction results from the intermediate layer of the network and integrating them with the final layer's predictions in a Bayesian fashion. Experimental evaluations conducted on image classification tasks using MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate the efficacy of our proposed method when applied to VGGNets and ResNets models. Results indicate a substantial energy reduction of 38.8% in VGGNets and 48.0% in ResNets, illustrating the potential for achieving significant efficiency gains in spiking neural networks. These findings contribute to the ongoing research in enhancing the performance of SNNs, facilitating their deployment in resource-constrained environments. Our code is available on GitHub: https://github.com/hanebarla/BayesianSpikeFusion.