Document Type
Article
Abstract
This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build and train hybrid models that integrate spiking neural networks (SNNs) and artificial neural networks (ANNs) using the PyTorch and Lava frameworks. We explore the effects of quantization on ANN models to assess its impact on both accuracy and energy efficiency. Additionally, we address the challenges of deploying hybrid models on hardware by implementing individual components on specific edge platforms. We also propose an accumulator circuit to bridge the spiking and non-spiking domains. Comprehensive performance analyses are conducted on a heterogeneous system of neuromorphic and edge AI hardware, assessing accuracy, latency, and energy consumption. Our results show that hybrid spiking networks improve accuracy and energy efficiency. Moreover, we find that quantization improves hybrid networks, further reducing energy consumption while boosting accuracy.
Digital Object Identifier (DOI)
Publication Info
Published in npj Unconventional Computing, Volume 2, 2025.
Rights
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/..
APA Citation
Seekings, J., Ardakani, M., Chandarana, P., Eslami, A., Mohammadi, M., & Zand, R. (2025). Integrated algorithm and hardware design for hybrid neuromorphic systems. npj Unconventional Computing, 2, Article 20. https://doi.org/10.1038/s44335-025-00036-2