Journal ArticleDOI
In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective
Yue Xi,Bin Gao,Jianshi Tang,An Chen,Meng-Fan Chang,Xiaobo Sharon Hu,Jan Van der Spiegel,He Qian,Huaqiang Wu +8 more
- Vol. 109, Iss: 1, pp 14-42
TLDR
This article defines the main figures of merit (FoMs) of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks.Abstract:
In this article, we review the existing analog resistive switching memory (RSM) devices and their hardware technologies for in-memory learning, as well as their challenges and prospects. Since the characteristics of the devices are different for in-memory learning and digital memory applications, it is important to have an in-depth understanding across different layers from devices and circuits to architectures and algorithms. First, based on a top-down view from architecture to devices for analog computing, we define the main figures of merit (FoMs) and perform a comprehensive analysis of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks. Second, we classify the FoMs of analog RSM devices into two levels. Level 1 FoMs are essential for achieving the functionality of a system (e.g., linearity, symmetry, dynamic range, level numbers, fluctuation, variability, and yield). Level 2 FoMs are those that make a functional system more efficient and reliable (e.g., area, operational voltage, energy consumption, speed, endurance, retention, and compatibility with back-end-of-line processing). By constructing a device-to-application simulation framework, we perform an in-depth analysis of how these FoMs influence in-memory learning and give a target list of the device requirements. Lastly, we evaluate the main FoMs of most existing devices with analog characteristics and review optimization methods from programming schemes to materials and device structures. The key challenges and prospects from the device to system level for analog RSM devices are discussed.read more
Citations
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Journal ArticleDOI
Memristive Crossbar Arrays for Storage and Computing Applications
Huihan Li,Shaocong Wang,Xumeng Zhang,Wei Wang,Rui Yang,Zhong Sun,Wanxiang Feng,Peng Lin,Zhongrui Wang,Linfeng Sun,Yugui Yao +10 more
TL;DR: Crossbar architecture is introduced, the origin of sneak‐path current is reviewed, techniques to mitigate this issue from the angle of materials and circuits are discussed, and the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed.
Journal ArticleDOI
High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing
Zhenwei Luo,Zijian Wang,Zeyu Guan,Chao Ma,Letian Zhao,Chuanchuan Liu,Haoyang Sun,He Wang,Yue Lin,Xi Jin,Yu Shizhuo Yin,Xiaoguang Li +11 more
TL;DR: In this paper , a high-performance synaptic device is designed and established based on a Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3 ferroelectric tunnel junction (FTJ).
Journal ArticleDOI
Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning
Mou Xing,Jianshi Tang,Yingjie Lyu,Qingtian Zhang,Siyao Yang,Feng Xu,Wei Liu,Minghong Xu,Yu Zhou,Wen Sun,Yanan Zhong,Bin Gao,Pu Yu,He Qian,Huaqiang Wu +14 more
TL;DR: In this article, a topotactic phase transition random access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoO x is demonstrated.
Journal ArticleDOI
Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses
Pengshan Xie,Yulong Huang,Wei Wang,You Meng,Zhengxun Lai,Fei Wang,SenPo Yip,Xiuming Bu,Weijun Wang,Dengji Li,Jia Sun,Johnny C. Ho +11 more
TL;DR: In this article, the poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE) wrapped InGaAs nanowire (NW) artificial synapses capable to operate with record-low subfemtojoule power consumption are presented.
Journal ArticleDOI
Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses
TL;DR: In this article , a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE) top-wrapped InGaAs nanowire (NW) artificial synapses capable to operate with record-low subfemtojoule power consumption is presented.
References
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Introduction To The Theory Of Neural Computation
TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.