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  • 标题:Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platfor
  • 本地全文:下载
  • 作者:Indar Sugiarto ; Steve Furber
  • 期刊名称:TELKOMNIKA (Telecommunication Computing Electronics and Control)
  • 印刷版ISSN:2302-9293
  • 出版年度:2021
  • 卷号:19
  • 期号:1
  • DOI:10.12928/telkomnika.v19i1.15026
  • 语种:English
  • 出版社:Universitas Ahmad Dahlan
  • 摘要:Genetic Algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multi- and many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker.
  • 关键词:full-duplex;outage probability;non-orthogonal multiple access (NOMA)
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