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Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array
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- Authors
- Hakcheon Jeong, Seungjae Han, See-On Park, Tae Ryong Kim, Jongmin Bae, Taehwan Jang, Yoonho Cho, Seokho Seo, Hyun-Jun Jeong, Seungwoo Park, Taehoon Park, Juyoung Oh, Jeongwoo Park, Kwangwon Koh, Kang-Ho Kim, Dongsuk Jeon, Inyong Kwon, Young-Gyu Yoon, Shinhyun Choi
- Issue Date
- 2025-02
- Citation
- Nature Electronics, v.8, no.2, pp.168-178
- ISSN
- 2520-1131
- Publisher
- Springer Nature
- Language
- English
- Type
- Journal Article
- Abstract
- Memristor-based platforms could be used to create compact and energy-efficient artificial intelligence (AI) edge-computing systems due to their parallel computation ability in the analogue domain. However, systems based on memristor arrays face challenges implementing real-time AI algorithms with fully on-device learning due to reliability issues, such as low yield, poor uniformity and endurance problems. Here we report an analogue computing platform based on a selector-less analogue memristor array. We use interfacial-type titanium oxide memristors with a gradual oxygen distribution that exhibit high reliability, high linearity, forming-free attribute and self-rectification. Our platform—which consists of a selector-less (one-memristor) 1 K (32 × 32) crossbar array, peripheral circuitry and digital controller—can run AI algorithms in the analogue domain by self-calibration without compensation operations or pretraining. We illustrate the capabilities of the system with real-time video foreground and background separation, achieving an average peak signal-to-noise ratio of 30.49 dB and a structural similarity index measure of 0.81; these values are similar to those of simulations for the ideal case.
- KSP Keywords
- Computing platform, Digital Controller, Edge Computing, Forming-free, High Reliability, High linearity, Oxygen distribution, Peak-Signal-to-Noise-Ratio(PSNR), Real-time video, Self-calibration, Self-rectification