Adaptive Timestep Improved Spiking Neural Network for Efficient Image Classification
Spiking neural network(SNN)has received broad attention for its relatively lower computational energy consumption compared to artificial neural network(ANN).However,most conventional SNNs use a synchronous computation paradigm,whereby multiple timesteps are commonly used to simulate the dynamic process of informa-tion integration,resulting in some problems such as extended inference delay and increased computational energy consumption,which lead to a serious efficiency discount during the realistic application of edge intelligent devices.In this paper,we propose an adaptive timestep improved spiking neural network(ATSNN)algorithm,which can automatically choose a proper inference timestep based on different features of input samples,and regulate the im-portance of different timesteps by designing an innovative time-dependent loss function.Besides,a low energy con-sumption SNN accelerator is designed based on the characteristics of ATSNN mentioned above to support applica-tions and deployments of ATSNN algorithm on some mature frameworks(such as VGG and ResNet).The results of software and hardware experiments on standard datasets such as CIFAR10,CIFAR100,and CIFAR10-DVS show that,compared to conventional SNN algorithms using static timesteps,the ATSNN algorithm can reach a compar-able accuracy but with a decreased inference delay(around 36.7％～58.7％)and reduced computational complexity(around 33.0％～57.0％).Furthermore,the running results on the hardware simulator indicate that the computa-tional energy consumption of ATSNN is only around 4.43％～7.88％of GPU RTX 3090Ti.It shows great advant-ages of brain-inspired neuromorphic hardware and software.
Spiking neural network(SNN)low power consumption inferenceefficient traininglow latency
万方数据
维普
