TELKOMNIKA Telecommunication, Computing, Electronics and Control
Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

Genetic algorithm
Network on chip
Neuromorphic computing
Parallel computing
SpiNNaker

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 par-
allelising their core processes. This paper describes a feasibility study of implement-
ing 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 over-
head. However, due to its small packets distribution mechanism and constrained pro-
cessing 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 benchmark-
ing test functions. The experiments produced some promising results that may lead to

further developments of massively parallel GAs on SpiNNaker.

Indar Sugiarto, Steve Furber

http://journal.uad.ac.id/index.php/TELKOMNIKA

Sep 24, 2020

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