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<p>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.</p>

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Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • 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

摘要

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.