MINI REVIEW article
Front. Neurosci.
Sec. Neuromorphic Engineering
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1676570
This article is part of the Research TopicNeuromorphic Synergy: Bridging Neuroscience and Electrical-Photonic Engineering for Next-Gen Computational and Sensing SolutionsView all articles
A Comparative Review of Deep and Spiking Neural Networks for Edge AI Neuromorphic Circuits
Provisionally accepted
- 1Université Savoie Mont Blanc, Chambéry, France
- 2Sorbonne Universite, Paris, France
- 3Shimane Daigaku, Matsue, Japan
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Edge AI implements neural networks directly in electronic circuits, using either deep neural networks (DNN) or neuromorphic spiking neural networks (SNN). DNNs offer high accuracy and easy-to-use tools but are computationally intensive and consume significant power. SNNs use bio-inspired, event-driven architectures that can be far more energy-efficient but rely on less mature training tools. This review surveys digital and analog edge-AI implementations, outlining device architectures, neuron models, and trade-offs in energy (J/OP), area (µm²/OP), and integration technology.
Keywords: energy efficiency, neuromorphic circuits, Edge AI, Spiking neural network (SNN), Deep Neural Networks (DNN)
Received: 30 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Maris Ferreira, Wang, Gao and Benlarbi-Delai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pietro Maris Ferreira, Université Savoie Mont Blanc, Chambéry, France
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