An energy-aware hybrid particle swarm optimization algorithm for Spiking Neural Network Mapping

Junxiu Liu, Xingyue Huang, Yuling Luo, Yi Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 mapping 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 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.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao, Yuanqing Li
PublisherSpringer Verlag
Pages805-815
Number of pages11
ISBN (Print)9783319700892
DOIs
Publication statusPublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10636 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Neural Information Processing, ICONIP 2017
Country/TerritoryChina
CityGuangzhou
Period14/11/1718/11/17

Keywords

  • genetic algorithm
  • networks-on-chip
  • particle swarm algorithm
  • Spiking Neural Networks

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