Design and Hardware Implementation of Neuromorphic Systems With RRAM Synapses and Threshold-Controlled Neurons for Pattern Recognition

被引：50

作者：

Jiang, Yuning ^{[1
]}

Huang, Peng ^{[1
]}

Zhu, Dongbin ^{[1
]}

Zhou, Zheng ^{[1
]}

Han, Runze ^{[1
]}

Liu, Lifeng ^{[1
]}

Liu, Xiaoyan ^{[1
]}

Kang, Jinfeng ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2018年 / 65卷 / 09期

关键词：

Neuromorphic system; metal-oxide RRAM; hardware implementation; pattern recognition; neuron circuits; RESISTIVE SWITCHING MEMORY; SPIKING NEURONS; ARRAY; DEVICE; NETWORK;

D O I：

10.1109/TCSI.2018.2812419

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a hardware-realized neuromorphic system for pattern recognition is presented. The system directly captures images from the environment, and then conducts classification using a single layer neural network. Metal-oxide resistive random access memory (RRAM) is used as electronic synapses, and threshold-controlled neurons are proposed as postsynaptic neurons to save the system area and simplify the operation. In the proposed threshold-controlled neuron, no capacitor is utilized, which contributes to higher integration density. The total energy consumption of RRAM synapses for classifying an example is 0.31 mu J on average. The proposed system has been implemented on hardware, and has been experimentally demonstrated to show the capability of pattern recognition.

引用

页码：2726 / 2738

页数：13

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