MOTPE/D: Hardware and Algorithm Co-design for Reconfigurable Neuromorphic Processor

Yuan Li, Renzhi Chen, Zhijie Yang, Xun Xiao, Jingyue Zhao, Zhenhua Zhu, Huadong Dai, Yuhua Tang, Weixia Xu, Li Luo, Lei Wang

Abstract: Recent advances in hardware/algorithm co-design for spiking neural networks have demonstrated its potential for jointly optimizing algorithmic performance while minimizing hardware overhead. However, the gigantic mixed-variable hard-ware/algorithm co-design space and time-consuming hardware verification still pose an intractable challenge for solutions exploration. To tackle these problems, 1) we propose a generic three-phase hardware/algorithm co-design framework. In this framework, 2) we target a reconfigurable neuromorphic processor, and parameterize the hardware and network architecture in a unified design space. 3) We propose a generic analytical model to estimate the parameter size and power consumption, which can support fast candidate evaluation during the exploration. 4) We extend vanilla TPE (a single-objective optimization algorithm) to MOTPE/D, a generic Multi-objective optimization (MOO) algorithm, by introducing a decomposition strategy.