Abstract
Recent approaches to improving the scalability of Spiking Neural Networks (SNNs) have looked to use custom architectures to implement 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 map** between a SNN and the NoC system becomes one of the most urgent challenges. In this paper, an energy-aware hybrid Particle Swarm Optimization (PSO) algorithm for SNN map** 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.
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Acknowledgments
This research was supported by the National Natural Science Foundation of China under Grants 61603104 and 61661008, the Guangxi Natural Science Foundation under Grant 2015GXNSFBA139256 and 2016GXNSFCA380017, the funding of Overseas 100 Talents Program of Guangxi Higher Education, the Research Project of Guangxi University of China under Grant KY2016YB059, Guangxi Key Lab of Multi-source Information Mining & Security under Grant MIMS15-07, and the Doctoral Research Foundation of Guangxi Normal University.