Event-based vision at the edge: A review

Middleton, Michael, Ali, Teymoor, Baikas, Epifanios, Kayan, Hakan, Sen Bhattacharya, Basabdatta, Gheorghiu, Elena, Vousden, Mark, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Rhodes, Oliver and Trefzer, Martin A. 2026. Event-based vision at the edge: A review. Brain Sciences 16 (4) , 422. 10.3390/brainsci16040422

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Abstract

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	MDPI
ISSN:	2076-3425
Date of First Compliant Deposit:	27 April 2026
Date of Acceptance:	15 April 2026
Last Modified:	07 Aug 2026 21:45
URI:	https://orca.cardiff.ac.uk/id/eprint/186661

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