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Neuromorphic Computing for Edge Applications
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Neuromorphic Computing for Edge Applications

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 766

Special Issue Editors

Dr. Luca Peres

E-Mail Website
Guest Editor
Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK
Interests: neuromorphic computing; SNNs; event-driven perception; low-energy computing; embedded systems; sensor fusion
Dr. Oliver Rhodes

E-Mail Website
Guest Editor
Department of Computer Science, University of Manchester, Manchester M13 9PL, UK
Interests: neuromorphic computing; spiking neural networks; real-time computing; low-power computing

Special Issue Information

Dear Colleagues,

Our society’s growing demand to integrate artificial intelligence in edge devices poses a significant challenge to traditional computing systems, which struggle to maintain low-power requirements while performing online real-time computational tasks. Neuromorphic computing, taking inspiration from biological computational principles, offers a valid low-energy alternative for real-time edge applications, bridging novel engineering principles with artificial intelligence.

This Special Issue aims to present the latest advances in low-energy neuromorphic technologies and algorithms for edge computing applications. Contributions in the form of original research articles, comprehensive review papers, and case studies are all welcome.

This Special Issue will cover topics including, but not limited to, the following:

  • Energy-efficient neuromorphic architectures for edge computing applications;
  • Event-based sensing technologies and algorithms;
  • Low-power sensor fusion applications;
  • Biologically inspired algorithms for low-power edge applications;
  • Emerging technologies and concepts for low-energy computing;
  • In/near-memory computing paradigms for edge applications;
  • Reversible neuromorphic architectures.

Dr. Luca Peres
Dr. Oliver Rhodes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuromorphic computing
  • low-energy computing
  • event-based sensing
  • spiking neural networks
  • edge computing
  • IoT

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Published Papers (1 paper)

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27 pages, 730 KB  
Article
Alleviating the Communication Bottleneck in Neuromorphic Computing with Custom-Designed Spiking Neural Networks
by James S. Plank, Charles P. Rizzo, Bryson Gullett, Keegan E. M. Dent and Catherine D. Schuman
J. Low Power Electron. Appl. 2025, 15(3), 50; https://doi.org/10.3390/jlpea15030050 - 8 Sep 2025
Abstract
For most, if not all, AI-accelerated hardware, communication with the agent is expensive and heavily bottlenecks the hardware performance. This omnipresent hardware restriction is also found in neuromorphic computing: a novel style of computing that involves deploying spiking neural networks to specialized hardware [...] Read more.
For most, if not all, AI-accelerated hardware, communication with the agent is expensive and heavily bottlenecks the hardware performance. This omnipresent hardware restriction is also found in neuromorphic computing: a novel style of computing that involves deploying spiking neural networks to specialized hardware to achieve low size, weight, and power (SWaP) compute. In neuromorphic computing, spike trains, times, and values are used to communicate information to, from, and within the spiking neural network. Input data, in order to be presented to a spiking neural network, must first be encoded as spikes. After processing the data, spikes are communicated by the network that represent some classification or decision that must be processed by decoder logic. In this paper, we first present principles for interconverting between spike trains, times, and values using custom-designed spiking subnetworks. Specifically, we present seven networks that encompass the 15 conversion scenarios between these encodings. We then perform three case studies where we either custom design a novel network or augment existing neural networks with these conversion subnetworks to vastly improve their communication performance with the outside world. We employ a classic space vs. time tradeoff by pushing spike data encoding and decoding techniques into the network mesh (increasing space) in order to minimize intra- and extranetwork communication time. This results in a classification inference speedup of 23× and a control inference speedup of 4.3× on field-programmable gate array hardware. Full article
(This article belongs to the Special Issue Neuromorphic Computing for Edge Applications)
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