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Advances in Low Power Neuromorphic Computing: Models, Algorithms, and Applications
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Advances in Low Power Neuromorphic Computing: Models, Algorithms, and Applications

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

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 4418

Special Issue Editors

Dr. Khanh N. Dang

E-Mail Website
Guest Editor
Adaptive Systems Laboratory, University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: three-dimensional integrated circuits; networks-on-chip; reliability; neuromorphic computing; computer architecture
Special Issues, Collections and Topics in MDPI journals
Dr. Nguyen Anh Vu Doan

E-Mail Website
Guest Editor
Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
Interests: design space exploration; design automation; multiobjective optimization; multicriteria decision aiding

Special Issue Information

Dear Colleagues,

Neuromorphic Computing (NC) lies at the intersection of neuroscience and artificial intelligence, where the aim is to provide cutting-edge computational capabilities inspired by the human brain, within the realm of modern technology. In certain applications such as edge or IoT devices, low-power NC is needed to achieve required lightweight computation targets, communication complexity, as well as green power/energy with satisfactory accuracy and quality in terms of algorithm, architecture, integrated circuit, system, standard, and application levels. This Special Issue aims to explore novel opportunities for the low-power processing of sensory data for cognitive applications using neuromorphic computing. The topics of interest include, but are not limited to, the following:

  • Neuromorphic computing for edge and IoT devices;
  • Optimization for neuromorphic computing;
  • Low-power solution for neuromorphic computing;
  • Applications for neuromorphic computing;
  • Architecture for neuromorphic computing;
  • Communication for neuromorphic computing;
  • Memory technology for neuromorphic computing;
  • Emerging topics of neuromorphic computing: photonics, three-dimensional circuits, in/near-memory computing.

Dr. Khanh N. Dang
Dr. Nguyen Anh Vu Doan
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 power
  • neural network
  • artificial intelligence
  • architecture

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 2103 KiB  
Article
Federated Multi-Stage Attention Neural Network for Multi-Label Electricity Scene Classification
by Lei Zhong, Xuejiao Jiang, Jialong Xu, Kaihong Zheng, Min Wu, Lei Gao, Chao Ma, Dewen Zhu and Yuan Ai
J. Low Power Electron. Appl. 2025, 15(3), 46; https://doi.org/10.3390/jlpea15030046 - 5 Aug 2025
Viewed by 251
Abstract
Privacy-sensitive electricity scene classification requires robust models under data localization constraints, making federated learning (FL) a suitable framework. Existing FL frameworks face two critical challenges in multi-label electricity scene classification: (1) Label correlations and their strengths significantly impact classification performance. (2) Electricity scene [...] Read more.
Privacy-sensitive electricity scene classification requires robust models under data localization constraints, making federated learning (FL) a suitable framework. Existing FL frameworks face two critical challenges in multi-label electricity scene classification: (1) Label correlations and their strengths significantly impact classification performance. (2) Electricity scene data and labels show distributional inconsistencies across regions. However, current FL frameworks lack explicit modeling of label correlation strengths, and locally trained regional models naturally capture these differences, leading to regional differences in their model parameters. In this scenario, the server’s standard single-stage aggregation often over-averages the global model’s parameters, reducing its discriminative ability. To address these issues, we propose FMMAN, a federated multi-stage attention neural network for multi-label electricity scene classification. The main contributions of this FMMAN lie in label correlation learning and the stepwise model aggregation. It splits the client–server interaction into multiple stages: (1) Clients train models locally to encode features and label correlation strengths after receiving the server’s initial model. (2) The server clusters these locally trained models into K groups to ensure that models within a group have more consistent parameters and generates K prototype models via intra-group aggregation to reduce over-averaging. The K models are then distributed back to the clients. (3) Clients refine their models using the K prototypes with contrastive group-specific consistency regularization to further mitigate over-averaging, and sends the refined model back to the server. (4) Finally, the server aggregates the models into a global model. Experiments on multi-label benchmarks verify that FMMAN outperforms baseline methods. Full article
(This article belongs to the Special Issue Advances in Low Power Neuromorphic Computing: Models, Algorithms, and Applications)
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27 pages, 1396 KiB  
Article
The Cart-Pole Application as a Benchmark for Neuromorphic Computing
by James S. Plank, Charles P. Rizzo, Chris A. White and Catherine D. Schuman
J. Low Power Electron. Appl. 2025, 15(1), 5; https://doi.org/10.3390/jlpea15010005 - 26 Jan 2025
Cited by 1 | Viewed by 1506
Abstract
The cart-pole application is a well-known control application that is often used to illustrate reinforcement learning algorithms with conventional neural networks. An implementation of the application from OpenAI Gym is ubiquitous and popular. Spiking neural networks are the basis of brain-based, or neuromorphic [...] Read more.
The cart-pole application is a well-known control application that is often used to illustrate reinforcement learning algorithms with conventional neural networks. An implementation of the application from OpenAI Gym is ubiquitous and popular. Spiking neural networks are the basis of brain-based, or neuromorphic computing. They are attractive, especially as agents for control applications, because of their very low size, weight and power requirements. We are motivated to help researchers in neuromorphic computing to be able to compare their work with common benchmarks, and in this paper we explore using the cart-pole application as a benchmark for spiking neural networks. We propose four parameter settings that scale the application in difficulty, in particular beyond the default parameter settings which do not pose a difficult test for AI agents. We propose achievement levels for AI agents that are trained with these settings. Next, we perform an experiment that employs the benchmark and its difficulty levels to evaluate the effectiveness of eight neuroprocessor settings on success with the application. Finally, we perform a detailed examination of eight example networks from this experiment, that achieve our goals on the difficulty levels, and comment on features that enable them to be successful. Our goal is to help researchers in neuromorphic computing to utilize the cart-pole application as an effective benchmark. Full article
(This article belongs to the Special Issue Advances in Low Power Neuromorphic Computing: Models, Algorithms, and Applications)
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21 pages, 3448 KiB  
Article
Optimizing Reservoir Separability in Liquid State Machines for Spatio-Temporal Classification in Neuromorphic Hardware
by Oscar I. Alvarez-Canchila, Andres Espinal, Alberto Patiño-Saucedo and Horacio Rostro-Gonzalez
J. Low Power Electron. Appl. 2025, 15(1), 4; https://doi.org/10.3390/jlpea15010004 - 24 Jan 2025
Viewed by 1718
Abstract
In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. By leveraging PSO, our method fine-tunes reservoir parameters, neuron dynamics, and connectivity patterns, maximizing separability [...] Read more.
In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. By leveraging PSO, our method fine-tunes reservoir parameters, neuron dynamics, and connectivity patterns, maximizing separability while aligning with the resource constraints typical of neuromorphic hardware. This approach was validated in both software (NEST) and on neuromorphic hardware (SpiNNaker), demonstrating notable results in terms of accuracy and low energy consumption when using SpiNNaker. Specifically, our approach addresses two problems: Frequency Recognition (FR) with five classes and Pattern Recognition (PR) with four, eight, and twelve classes. For instance, in the Mono-objective approach running in NEST, accuracies ranged from 81.09% to 95.52% across the benchmarks under study. The Multi-objective approach outperformed the Mono-objective approach, delivering accuracies ranging from 90.23% to 98.77%, demonstrating its superior scalability for LSM implementations. On the SpiNNaker platform, the mono-objective approach achieved accuracies ranging from 86.20% to 97.70% across the same benchmarks, with the Multi-objective approach further improving accuracies, ranging from 94.42% to 99.52%. These results show that, in addition to slight accuracy improvements, hardware-based implementations offer superior energy efficiency with a lower execution time. For example, SpiNNaker operates at around 1–5 watts per chip, while traditional systems can require 50–100 watts for similar tasks, highlighting the significant energy savings of neuromorphic hardware. These results underscore the scalability and effectiveness of PSO-optimized LSMs on resource-limited neuromorphic platforms, showcasing both improved classification performance and the advantages of energy-efficient processing. Full article
(This article belongs to the Special Issue Advances in Low Power Neuromorphic Computing: Models, Algorithms, and Applications)
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