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

Junxiu Liu; Xingyue Huang; Yuling Luo; Yi Cao

doi:10.1007/978-3-319-70090-8_82

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

Junxiu Liu
, Xingyue Huang
, Yuling Luo^*
, Yi Cao

^*Corresponding author for this work

Guangxi Normal University
University of Surrey

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

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 language	English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Derong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao, Yuanqing Li
Publisher	Springer Verlag
Pages	805-815
Number of pages	11
ISBN (Print)	9783319700892
DOIs	https://doi.org/10.1007/978-3-319-70090-8_82
Publication status	Published - 2017
Externally published	Yes
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10636 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Neural Information Processing, ICONIP 2017
Country/Territory	China
City	Guangzhou
Period	14/11/17 → 18/11/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Genetic Algorithm
Networks-on-Chip
Particle swarm algorithm
Spiking Neural Networks

Access to Document

10.1007/978-3-319-70090-8_82

Cite this

Liu, J., Huang, X., Luo, Y., & Cao, Y. (2017). An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping. In D. Liu, S. Xie, E.-S. M. El-Alfy, D. Zhao, & Y. Li (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 805-815). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10636 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70090-8_82

Liu, Junxiu ; Huang, Xingyue ; Luo, Yuling et al. / An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Derong Liu ; Shengli Xie ; El-Sayed M. El-Alfy ; Dongbin Zhao ; Yuanqing Li. Springer Verlag, 2017. pp. 805-815 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b3d3bbcfa57f406ca95d7265dd35e9ae,

title = "An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping",

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.",

keywords = "Genetic Algorithm, Networks-on-Chip, Particle swarm algorithm, Spiking Neural Networks",

author = "Junxiu Liu and Xingyue Huang and Yuling Luo and Yi Cao",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",

year = "2017",

doi = "10.1007/978-3-319-70090-8\_82",

language = "English",

isbn = "9783319700892",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "805--815",

editor = "Derong Liu and Shengli Xie and El-Alfy, \{El-Sayed M.\} and Dongbin Zhao and Yuanqing Li",

booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",

}

Liu, J, Huang, X, Luo, Y & Cao, Y 2017, An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping. in D Liu, S Xie, E-SM El-Alfy, D Zhao & Y Li (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10636 LNCS, Springer Verlag, pp. 805-815, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14/11/17. https://doi.org/10.1007/978-3-319-70090-8_82

An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping. / Liu, Junxiu; Huang, Xingyue; Luo, Yuling et al.
Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Derong Liu; Shengli Xie; El-Sayed M. El-Alfy; Dongbin Zhao; Yuanqing Li. Springer Verlag, 2017. p. 805-815 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10636 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping

AU - Liu, Junxiu

AU - Huang, Xingyue

AU - Luo, Yuling

AU - Cao, Yi

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Genetic Algorithm

KW - Networks-on-Chip

KW - Particle swarm algorithm

KW - Spiking Neural Networks

UR - https://www.scopus.com/pages/publications/85035209403

U2 - 10.1007/978-3-319-70090-8_82

DO - 10.1007/978-3-319-70090-8_82

M3 - Conference Proceeding

AN - SCOPUS:85035209403

SN - 9783319700892

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 805

EP - 815

BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings

A2 - Liu, Derong

A2 - Xie, Shengli

A2 - El-Alfy, El-Sayed M.

A2 - Zhao, Dongbin

A2 - Li, Yuanqing

PB - Springer Verlag

T2 - 24th International Conference on Neural Information Processing, ICONIP 2017

Y2 - 14 November 2017 through 18 November 2017

ER -

Liu J, Huang X, Luo Y, Cao Y. An Energy-Aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping. In Liu D, Xie S, El-Alfy ESM, Zhao D, Li Y, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 805-815. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70090-8_82

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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