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

IF 33.7 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

Nature Electronics Pub Date : 2025-01-08 DOI:10.1038/s41928-024-01318-6

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

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引用次数: 0

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.

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基于无选择器忆阻器阵列的自监督视频自校准模拟计算平台

基于忆阻器的平台由于其在模拟域的并行计算能力，可以用于创建紧凑和节能的人工智能（AI）边缘计算系统。然而，由于可靠性问题，如成品率低、均匀性差和耐用性问题，基于忆阻器阵列的系统在实现完全设备上学习的实时人工智能算法方面面临挑战。本文报道了一种基于无选择器模拟忆阻器阵列的模拟计算平台。我们采用氧分布渐变的界面型氧化钛忆阻器，具有高可靠性、高线性度、无形成特性和自整流特性。我们的平台由一个无选择器（一个忆阻器）1k （32 × 32）交叉棒阵列、外围电路和数字控制器组成，可以通过自校准在模拟域中运行AI算法，而无需补偿操作或预训练。我们展示了该系统在实时视频前景和背景分离方面的能力，实现了平均峰值信噪比为30.49 dB，结构相似指数为0.81；这些值与理想情况下的模拟值相似。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Electronics Engineering-Electrical and Electronic Engineering

CiteScore

47.50

自引率

2.30%

发文量

159

期刊介绍： Nature Electronics is a comprehensive journal that publishes both fundamental and applied research in the field of electronics. It encompasses a wide range of topics, including the study of new phenomena and devices, the design and construction of electronic circuits, and the practical applications of electronics. In addition, the journal explores the commercial and industrial aspects of electronics research. The primary focus of Nature Electronics is on the development of technology and its potential impact on society. The journal incorporates the contributions of scientists, engineers, and industry professionals, offering a platform for their research findings. Moreover, Nature Electronics provides insightful commentary, thorough reviews, and analysis of the key issues that shape the field, as well as the technologies that are reshaping society. Like all journals within the prestigious Nature brand, Nature Electronics upholds the highest standards of quality. It maintains a dedicated team of professional editors and follows a fair and rigorous peer-review process. The journal also ensures impeccable copy-editing and production, enabling swift publication. Additionally, Nature Electronics prides itself on its editorial independence, ensuring unbiased and impartial reporting. In summary, Nature Electronics is a leading journal that publishes cutting-edge research in electronics. With its multidisciplinary approach and commitment to excellence, the journal serves as a valuable resource for scientists, engineers, and industry professionals seeking to stay at the forefront of advancements in the field.