Lightweight error-tolerant edge detection using memristor-enabled stochastic computing

Lekai Song, Pengyu Liu, Jingfang Pei, Yang Liu, Songwei Liu, Shengbo Wang, Leonard W. T. Ng, Tawfique Hasan, Kong-Pang Pun, Shuo Gao and Guohua Hu ()
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Lekai Song: The Chinese University of Hong Kong
Pengyu Liu: The Chinese University of Hong Kong
Jingfang Pei: The Chinese University of Hong Kong
Yang Liu: The Chinese University of Hong Kong
Songwei Liu: The Chinese University of Hong Kong
Shengbo Wang: Beihang University
Leonard W. T. Ng: Nanyang Technological University
Tawfique Hasan: University of Cambridge
Kong-Pang Pun: The Chinese University of Hong Kong
Shuo Gao: Beihang University
Guohua Hu: The Chinese University of Hong Kong

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract The demand for efficient edge computer vision has spurred the development of stochastic computing for image processing. Memristors, by introducing their inherent switching stochasticity into computation, readily enable stochastic image processing. Here, we present a lightweight, error-tolerant edge detection approach based on memristor-enabled stochastic computing. By integrating memristors into compact logic circuits, we realise lightweight stochastic logics for stochastic number encoding and processing with well-regulated probabilities and correlations. This stochastic and probabilistic computational nature allows the stochastic logics to perform edge detection in edge visual scenarios characterised by high-level errors. As a demonstration, we implement a hardware edge detection operator using the stochastic logics, and prove its exceptional performance with 95% less energy consumption while withstanding 50% bit-flips. The results underscore the potential of our stochastic edge detection approach for developing efficient edge visual hardware for autonomous driving, virtual and augmented reality, medical imaging diagnosis, and beyond.

Date: 2025
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DOI: 10.1038/s41467-025-59872-2

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