Lightweight error-tolerant edge detection using memristor-enabled stochastic computing
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-toler...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59872-2 |
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| author | Lekai Song Pengyu Liu Jingfang Pei Yang Liu Songwei Liu Shengbo Wang Leonard W. T. Ng Tawfique Hasan Kong-Pang Pun Shuo Gao Guohua Hu |
| author_facet | Lekai Song Pengyu Liu Jingfang Pei Yang Liu Songwei Liu Shengbo Wang Leonard W. T. Ng Tawfique Hasan Kong-Pang Pun Shuo Gao Guohua Hu |
| author_sort | Lekai Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4da4b00464674a9e9b2684248d00f032 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-4da4b00464674a9e9b2684248d00f0322025-08-20T02:25:17ZengNature PortfolioNature Communications2041-17232025-05-011611910.1038/s41467-025-59872-2Lightweight error-tolerant edge detection using memristor-enabled stochastic computingLekai Song0Pengyu Liu1Jingfang Pei2Yang Liu3Songwei Liu4Shengbo Wang5Leonard W. T. Ng6Tawfique Hasan7Kong-Pang Pun8Shuo Gao9Guohua Hu10Department of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongDepartment of Electronic Engineering, The Chinese University of Hong KongSchool of Instrumentation and Optoelectronic Engineering, Beihang UniversitySchool of Materials Science and Engineering, Nanyang Technological UniversityCambridge Graphene Centre, University of CambridgeDepartment of Electronic Engineering, The Chinese University of Hong KongSchool of Instrumentation and Optoelectronic Engineering, Beihang UniversityDepartment of Electronic Engineering, The Chinese University of Hong KongAbstract 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.https://doi.org/10.1038/s41467-025-59872-2 |
| spellingShingle | Lekai Song Pengyu Liu Jingfang Pei Yang Liu Songwei Liu Shengbo Wang Leonard W. T. Ng Tawfique Hasan Kong-Pang Pun Shuo Gao Guohua Hu Lightweight error-tolerant edge detection using memristor-enabled stochastic computing Nature Communications |
| title | Lightweight error-tolerant edge detection using memristor-enabled stochastic computing |
| title_full | Lightweight error-tolerant edge detection using memristor-enabled stochastic computing |
| title_fullStr | Lightweight error-tolerant edge detection using memristor-enabled stochastic computing |
| title_full_unstemmed | Lightweight error-tolerant edge detection using memristor-enabled stochastic computing |
| title_short | Lightweight error-tolerant edge detection using memristor-enabled stochastic computing |
| title_sort | lightweight error tolerant edge detection using memristor enabled stochastic computing |
| url | https://doi.org/10.1038/s41467-025-59872-2 |
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