Journal ArticleDOI
The future of electronics based on memristive systems
Mohammed A. Zidan,John Paul Strachan,Wei Lu +2 more
- Vol. 1, Iss: 1, pp 22-29
TLDR
The state of the art in memristor-based electronics is evaluated and the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing is explored.Abstract:
A memristor is a resistive device with an inherent memory. The theoretical concept of a memristor was connected to physically measured devices in 2008 and since then there has been rapid progress in the development of such devices, leading to a series of recent demonstrations of memristor-based neuromorphic hardware systems. Here, we evaluate the state of the art in memristor-based electronics and explore where the future of the field lies. We highlight three areas of potential technological impact: on-chip memory and storage, biologically inspired computing and general-purpose in-memory computing. We analyse the challenges, and possible solutions, associated with scaling the systems up for practical applications, and consider the benefits of scaling the devices down in terms of geometry and also in terms of obtaining fundamental control of the atomic-level dynamics. Finally, we discuss the ways we believe biology will continue to provide guiding principles for device innovation and system optimization in the field. This Perspective evaluates the state of the art in memristor-based electronics and explores the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing.read more
Citations
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Journal ArticleDOI
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
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Electronic Skin: Recent Progress and Future Prospects for Skin‐Attachable Devices for Health Monitoring, Robotics, and Prosthetics
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Resistive switching materials for information processing
Zhongrui Wang,Huaqiang Wu,Geoffrey W. Burr,Cheol Seong Hwang,Kang L. Wang,Qiangfei Xia,Jianhua Yang +6 more
TL;DR: This Review surveys the four physical mechanisms that lead to resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck and examines the device requirements for systems based on RSMs.
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Designing crystallization in phase-change materials for universal memory and neuro-inspired computing
TL;DR: This Review focuses on the crystallization mechanisms of PCMs as well as the design principles to achieve PCMs with high switching speeds and good data retention, which will aid the development of PCM-based universal memory and neuro-inspired devices.
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A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations
Fuxi Cai,Justin M. Correll,Seung Hwan Lee,Yong Lim,Yong Lim,Vishishtha Bothra,Zhengya Zhang,Michael P. Flynn,Wei Lu +8 more
TL;DR: A programmable neuromorphic computing chip based on passive memristor crossbar arrays integrated with analogue and digital components and an on-chip processor enables the implementation of neuromorphic and machine learning algorithms.
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