RRAM-Based Binary Neural Networks Using Back-Propagation Learning

Yuning Jiang; Yunfan Yang; Zheng Zhou; Yachen Xiang; Peng Huang; Jinfeng Kang

doi:10.1109/icsict.2018.8564972

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JOURNAL ARTICLE

RRAM-Based Binary Neural Networks Using Back-Propagation Learning

Yuning Jiang Yunfan Yang Zheng Zhou Yachen Xiang Peng Huang Jinfeng Kang

Year: 2018 Vol: 100 Pages: 1-3

DOI: 10.1109/icsict.2018.8564972

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Abstract

Hardware binary neural networks (BNNs) based on resistive random access memory (RRAM) are designed and investigated in this work. RRAM devices that work in binary mode are used as electronic synapses. The simulation results indicate that the designed BNNs can achieve an accuracy of 94% on the MNIST database, and show remarkable tolerance to non-ideal properties of RRAM-based electronic synapses.

Keywords:

Resistive random-access memory MNIST database Binary number Artificial neural network Computer science Resistive touchscreen Work (physics) Artificial intelligence Ideal (ethics) Non-volatile memory Machine learning Computer architecture Electrical engineering Voltage Computer hardware Engineering Mathematics Computer vision

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Advanced Memory and Neural Computing

Physical Sciences → Engineering → Electrical and Electronic Engineering

Ferroelectric and Negative Capacitance Devices

Physical Sciences → Engineering → Electrical and Electronic Engineering

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

RRAM-Based Binary Neural Networks Using Back-Propagation Learning

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