A hardware impulse neural network system

Abstract

The invention discloses a hardware impulse neural network system, comprising: an input node layer and an unsupervised learning layer are connected through a synaptic connection unit in a neuron full connection mode; the unsupervised learning layer and the supervised learning layer are connected through another synaptic connection unit in a neuron full connection mode; the input node layer and theunsupervised learning layer are connected through a synaptic connection unit in a synaptic connection mode. The input node layer realizes the information input under different coding modes, the non-supervisory learning layer adopts the non-supervisory learning mode, and the supervisory learning layer adopts the supervisory learning mode. A synaptic connection unit is realized by an electronic synaptic device, so that that synaptic connection unit has a pulse time dependent plasticity STDP. The synaptic array unit receives as presynaptic pulses the stimulation signals from the neurons in the front layer and the postsynaptic pulses the action potential pulses excited by the neurons in the back layer. The time difference between the presynaptic pulses and the postsynaptic pulses determines the synaptic weight adjustment amount of the synaptic connection unit. The neural network system provided by the invention has a wide application value.

Description

[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 context of today's ...

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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