记忆电阻器
计算机科学
电阻随机存取存储器
神经形态工程学
纳米技术
领域(数学)
功率消耗
CMOS芯片
计算机体系结构
电气工程
功率(物理)
人工智能
工程类
材料科学
人工神经网络
物理
数学
量子力学
电压
纯数学
作者
Min‐Kyu Song,Ji‐Hoon Kang,Xinyuan Zhang,Wonjae Ji,Alon Ascoli,Ioannis Messaris,Ahmet Şamil Demirkol,Bowei Dong,Samarth Aggarwal,Weier Wan,Seokman Hong,Suma Cardwell,Irem Boybat,Jae-sun Seo,Jang‐Sik Lee,Mario Lanza,Han‐Wool Yeon,Murat Onen,Ju Li,Bilge Yildiz
出处
期刊:ACS Nano
[American Chemical Society]
日期:2023-06-29
卷期号:17 (13): 11994-12039
被引量:127
标识
DOI:10.1021/acsnano.3c03505
摘要
Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.
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