Abstract
Recent approaches to improving the scalability of Spiking Neural Networks (SNNs) have looked to use custom architectures to im- plement and interconnect the neurons in the hardware. The Networks- on-Chip (NoC) interconnection strategy has been used for the hardware SNNs and has achieved a good performance. However, the mapping be- tween a SNN and the NoC system becomes one of the most urgent chal- lenges. In this paper, an energy-aware hybrid Particle Swarm Optimiza- tion (PSO) algorithm for SNN mapping is proposed, which combines the basic PSO and Genetic Algorithm (GA). A Star-Subnet-Based-2D Mesh (2D-SSBM) NoC system is used for the testing. Results show that the proposed hybrid PSO algorithm can avoid the premature convergence to local optimum, and effectively reduce the energy consumption of the hardware NoC systems.