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An Energy-aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping

Liu, Junxiu, Huang, Xingyue, Luo, Yuling and Cao, Yi (2017) An Energy-aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping In: The 24th International Conference on Neural Information Processing (ICONIP 2017), 14-18 Nov 2017, Guanzhou, China.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Liu, JunxiuUNSPECIFIEDUNSPECIFIED
Huang, XingyueUNSPECIFIEDUNSPECIFIED
Luo, YulingUNSPECIFIEDUNSPECIFIED
Cao, Yiyc0006@surrey.ac.ukUNSPECIFIED
Date : 2017
Uncontrolled Keywords : Particle Swarm Algorithm Genetic Algorithm; Spiking Neural Networks; Networks-on-Chip
Related URLs :
Depositing User : Clive Harris
Date Deposited : 19 Sep 2017 14:29
Last Modified : 18 Nov 2017 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/842324

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