AUTHOR=M. Ferreira Pietro , Wang Siqi , Gao Yueyuan , Benlarbi-Delai Aziz 

TITLE=A comparative review of deep and spiking neural networks for edge AI neuromorphic circuits

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1676570

DOI=10.3389/fnins.2025.1676570

ISSN=1662-453X

ABSTRACT=Edge AI implements neural networks directly in electronic circuits, using either deep neural networks (DNNs) or neuromorphic spiking neural networks (SNNs). DNNs offer high accuracy and easy-to-use tools but are computationally intensive and consume significant power. SNNs utilize bio-inspired, event-driven architectures that can be significantly more energy-efficient, but they 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.