A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

被引：467

作者：

Yu, Shimeng ^{[1
,2
]}

Gao, Bin ^{[3
]}

Fang, Zheng ^{[5
]}

Yu, Hongyu ^{[4
]}

Kang, Jinfeng ^{[3
]}

Wong, H. -S. Philip ^{[1
,2
]}

机构：

[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

[2] Stanford Univ, Ctr Integrated Syst, Stanford, CA 94305 USA

[3] Peking Univ, Inst Microelect, Beijing 100871, Peoples R China

[4] South Univ Sci & Technol China, Shenzhen 518055, Peoples R China

[5] ASTAR, Inst Microelect, Singapore 117685, Singapore

来源：

ADVANCED MATERIALS | 2013年 / 25卷 / 12期

关键词：

resistive switching; oxide RRAM; synaptic devices; neuromorphic computing; artificial visual systems; SWITCHING PARAMETER VARIATION; SPIKING NEURONS; PLASTICITY; SYNAPSES; DESIGN; MEMORY;

D O I：

10.1002/adma.201203680

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation. [GRAPHICS] .

引用

页码：1774 / 1779

页数：6

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