A hardware spiking neural network system

A pulse neural network and hardware technology, applied in the field of artificial neural network, can solve the problems of poor robustness, inability to meet the STDP characteristics of electronic synapse devices, and low recognition rate, and achieve the effect of improving recognition rate and robustness

Active Publication Date: 2020-10-16
HUAZHONG UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0006] Aiming at the defects of the prior art, the purpose of the present invention is to combine the supervised and unsupervised learning methods, as well as the brain-like mechanism of side inhibition and pulse synchronous distribution, to solve the problem of robustness in the existing network training methods of the spiking neural network. The performance is poor, the recognition rate is low, and the STDP characteristics of various types of electronic synaptic devices cannot be satisfied at the same time. The technical problems of hardware application

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  • A hardware spiking neural network system
  • A hardware spiking neural network system
  • A hardware spiking neural network system

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Embodiment Construction

[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0038] The invention proposes a pulse neural network system based on an electronic synaptic device, and is committed to developing a pulse neural network system with application value and advantages. The invention discloses a hardware pulse neural network model design based on an electronic synapse device. Based on the bionic synaptic characteristics of electronic synaptic devices, such as long...

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Abstract

The invention discloses a hardware pulse neural network system, comprising: the input node layer and the unsupervised learning layer are connected through a synaptic connection unit through a neuron full connection mode, and the unsupervised learning layer and the supervised learning layer are connected by a neuron full connection The method is connected through another synaptic connection unit; the input node layer realizes the information input under different encoding methods, the unsupervised learning layer adopts the unsupervised learning method, and the supervised learning layer adopts the supervised learning method; the synaptic connection unit is composed of electronic The synaptic device is implemented so that the synaptic connection unit has pulse timing-dependent plasticity STDP, and the synaptic array unit receives the stimulus signal carrying information from the previous layer of neurons as a presynaptic pulse, combined with the actions excited by the subsequent layer of neurons The potential pulse acts as a post-synaptic pulse, and the time difference between the pre-synaptic pulse and the post-synaptic pulse determines the amount of synaptic weight adjustment of the synaptic connection unit. The neural network system provided by the invention has wide application value.

Description

technical field [0001] The present invention relates to the technical field of artificial neural network, and more specifically, relates to a hardware impulse neural network system. Background technique [0002] In the context of the era of big data, the traditional data processing method in which memory and processor are separated has brought about the von Neumann bottleneck problem, and this processing method has gradually been unable to meet people's growing data processing needs. However, the information activities of the human brain nervous system have the characteristics of large-scale parallelism, distributed storage and processing, self-organization, self-adaptation and self-learning, and there is no obvious boundary between data storage and processing. Theories, methods, technologies and application systems that expand and expand human intelligence, as well as the simulation of the information process of human consciousness and thinking, have great potential in the ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/06
CPCG06N3/061G06N3/065
Inventor 缪向水陈佳李祎秦超
Owner HUAZHONG UNIV OF SCI & TECH
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