HyNITA: A Neuromorphic Inference and Training Accelerator for Hybrid ANN-SNN Fusion Models

Yi Zhong, Li Lun, Zilin Wang, Jinhao Ruan, Yipeng Gao, Xiaoxin Cui, Xing Zhang, Yuan Wang

Abstract: In order to achieve the brain-like advantages over conservative computers, previous neuromorphic researchers have stretched the hardware explorations of the hybrid artificial neural network (ANN) and spiking neural network (SNN) inference approaches, as well as the efficient bio-plausible and gradient-based SNN training mechanisms. However, a versatile accelerator for both ANN-SNN inference and training is little addressed. In this work, we introduce HyNITA, a neuromorphic processor that supports accelerating both inference and training tasks of hybrid ANN and SNN models. Regarding the similarity and distinction, a pair of working stages are distinguished and distributed to multiple simple cores. The accelerator optimizes the interchange dataflow in a scalable chip design, following a reconfigurable design methodology to integrate the involved equation calculations in the dynamic process of neurons. The evaluation results show it achieves an accuracy of 99.65% and 99.34% on training ANN MNIST and SNN N-MNIST datasets.

External IDs:dblp:conf/iscas/ZhongLWRGCZW25