An Energy-aware Spiking Neural Network Hardware Mapping based on Particle Swarm Optimization and Genetic Algorithm

Abstract

Spiking Neuron Network (SNN) is a biological neural network model which shows great capability in the time series data processing and pattern recognition etc. according to the recent research. It has been implemented in hardware system with a good scalability, where the Networks-on-Chip (NoC) interconnection strategy is widely used for the data communications between the neurons. The mapping between a SNN and a NoC hardware system is one of the challenge for the development of the hardware SNNs. In this paper, a hybrid Particle Swarm Optimization (PSO) algorithm for hardware SNN mapping is proposed with the object of minimizing the energy consumption. Compared to the conventional PSO, it can search the mapping solutions through three directions which can speed up the finding. In the meantime, the Genetic Algorithm (GA) is combined to provide the mutation operation to avoid converging to the local optimum. A typical hardware SNN is used as the testbench and results show that an effective hardware SNN mapping is obtained with a low energy consumption, and local optimum is avoided compared to other approaches.