GDL

Journal from gdlhub / 2021-09-04 15:47:59
Oleh : Indar Sugiarto, Steve Furber, Telkomnika
Dibuat : 2021-01-29, dengan 1 file

Keyword : network on chip; neuromorphic computing; parallel genetic algorithms; SpiNNaker;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/15026
Sumber pengambilan dokumen : web

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.

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