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

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 synapse...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2018-09, Vol.65 (9), p.2726-2738
Main Authors: Jiang, Yuning, Huang, Peng, Zhu, Dongbin, Zhou, Zheng, Han, Runze, Liu, Lifeng, Liu, Xiaoyan, Kang, Jinfeng
Format: Article
Language:English
Subjects:
Biological neural networks
Energy consumption
Hardware
hardware implementation
Image classification
Metal oxides
metal-oxide RRAM
Neural networks
Neuromorphic system
Neuromorphics
neuron circuits
Neurons
Object recognition
Pattern recognition
Random access memory
Synapses
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Summary: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 0.31μJ on average. The proposed system has been implemented on hardware, and has been experimentally demonstrated to show the capability of pattern recognition.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2018.2812419