Spiking Neural Networks in Intelligent Edge Computing
Zhang, Guanlei; Feng, Lei; Zhou, Fanqin; Yang, Zhixiang; Zhang, Qiyang; Saleh, Alaa; Donta, Praveen Kumar; Dehury, Chinmaya Kumar (2024-11-25)
IEEE
25.11.2024
G. Zhang et al., "Spiking Neural Networks in Intelligent Edge Computing," in IEEE Consumer Electronics Magazine, doi: 10.1109/MCE.2024.3506502
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503101934
https://urn.fi/URN:NBN:fi:oulu-202503101934
Tiivistelmä
Abstract
Deep neural networks (DNNs) have witnessed rapid advancements and remarkable success in recent years, leading to their increasingly widespread implementation on edge devices. However, the deployment, execution, and life cycle management of traditional artificial neural networks (ANNs) on resource-constrained edge devices present significant challenges. Spiking neural networks (SNNs) are a class of neuroscience-inspired neural networks that emulate the low-power operational mode of biological neurons. SNNs possess advantages such as low power consumption, low latency, event-driven processing, and reduced communication overhead, making them particularly well-suited for edge devices and intelligent edge computing. As a result, they have garnered significant attention in both research and practical applications. In this paper, we present a comprehensive survey of the fundamentals of SNNs and the advancements in SNN research for edge computing, exploring potential applications and future directions in this emerging field. We also present a case study highlighting that SNNs outperform ANNs in distributed learning, achieving a 6% improvement in accuracy and an 80% reduction in data transmission.
Deep neural networks (DNNs) have witnessed rapid advancements and remarkable success in recent years, leading to their increasingly widespread implementation on edge devices. However, the deployment, execution, and life cycle management of traditional artificial neural networks (ANNs) on resource-constrained edge devices present significant challenges. Spiking neural networks (SNNs) are a class of neuroscience-inspired neural networks that emulate the low-power operational mode of biological neurons. SNNs possess advantages such as low power consumption, low latency, event-driven processing, and reduced communication overhead, making them particularly well-suited for edge devices and intelligent edge computing. As a result, they have garnered significant attention in both research and practical applications. In this paper, we present a comprehensive survey of the fundamentals of SNNs and the advancements in SNN research for edge computing, exploring potential applications and future directions in this emerging field. We also present a case study highlighting that SNNs outperform ANNs in distributed learning, achieving a 6% improvement in accuracy and an 80% reduction in data transmission.
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