Study on neuromorphic computation and its applications
Abstract
Neuromorphic computing offers a promising alternative to traditional von Neumann architectures, especially for applications that require efficient processing in edge environments. The challenge lies in optimizing spiking neural networks (SNNs) for these environments to achieve high computational efficiency, particularly in event-driven applications. This paper investigates the integration of advanced simulation tools, such as Simeuro and SuperNeuro, to enhance SNN performance on edge devices. Through comprehensive studies of various SNN models, a novel SNN design with optimized hardware components is proposed, focusing on energy and communication efficiency. The results demonstrate significant improvements in computational efficiency and performance, validating the potential of neuromorphic architectures for executing event-driven scientific applications. The findings suggest that neuromorphic computing can transform the way edge devices handle event-driven tasks, offering a pathway for future innovations in diverse application domains.
Keywords
Event-driven applications; Neuromorphic computing; SimEuro; Spiking neural networks; SuperNeuro
Full Text:
PDFDOI: http://doi.org/10.11591/ijeecs.v39.i1.pp272-282
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES).