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.
@article{arxiv.2605.13699,
title = {Memristor Technologies for Dynamic Vision Sensors: A Critical Assessment and Research Roadmap},
author = {Mohamad Yazan Sadoun and Edris Zaman Farsa and Sarah Sharif and Yaser Mike Banad},
journal= {arXiv preprint arXiv:2605.13699},
year = {2026}
}