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arXiv · 2605.13699

Memristor Technologies for Dynamic Vision Sensors: A Critical Assessment and Research Roadmap

Mohamad Yazan Sadoun·Edris Zaman Farsa·Sarah Sharif·Yaser Mike Banad
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Abstract

Edge-AI deployment is bottlenecked by data-movement energy; pairing event-driven vision sensors with in-memory analog compute could lift that ceiling by orders of magnitude. Both technologies are individually mature; the framework distinguishing fabricated demonstrations from projected systems is missing. Of six application domains surveyed (robotics, autonomous vehicles, AR/VR, surveillance, medical imaging, IoT), half rest entirely on projection, and existing hardware sits at Technology Readiness Levels 2-5. This evidence-graded review applies a three-paradigm architectural taxonomy and benchmarks the gap against current digital neuromorphic alternatives. It identifies an end-to-end integrated DVS-memristor system as the field's open challenge, with testable accuracy and power targets.

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Mohamad Yazan Sadoun, Edris Zaman Farsa, Sarah Sharif, Yaser Mike Banad. 2026-05-13. Memristor Technologies for Dynamic Vision Sensors: A Critical Assessment and Research Roadmap. https://arxiv.org/abs/2605.13699

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