Full-digital pulse neural network hardware system and method based on STDP rule

Abstract

The invention discloses a full-digital pulse neural network hardware system and a full-digital pulse neural network hardware method based on an STDP rule, which organically combine the STDP rule in neurobiology and the advantage of parallelized data processing in a digital circuit, and can efficiently complete training and identification tasks on an image data set in the field of image processing. The spiking neural network system comprises an input layer neuron module, a plasticity learning module, a data line control module, a synaptic array module, an output layer neuron module and an experiment report module. The output end of the output layer neuron module is connected with the input end of the data line control module and the input end of the plasticity learning module, the output end of the plasticity learning module is connected with the input end of the data line control module, and the output end of the data line control module is connected with the input end of the synapse array module. The output end of the synapse array module is connected with the input end of the output layer neurons, and the output end of the output layer neurons is connected with the input end of the experiment report module.

Description

technical field [0001] The invention relates to the field of brain-inspired pulse neural networks, in particular to an all-digital pulse neural network hardware system and method based on the STDP rule. [0002] technical background [0003] Neurological research has made remarkable progress in recent years, and many new properties of biological neurons have been discovered. Biological neurons have dynamic and spatiotemporal characteristics when processing information, which is embodied in the high efficiency and low energy consumption of real-time information processing. With the development of a highly informationized society, the requirements for processing real-time data continue to increase. Therefore, software models and hardware architectures that imitate neuron working methods have become the focus of research. At present, IBM's Zhengbei chip, Inter's Loihi chip, Tsinghua University's Tianji chip, and Zhejiang University's Darwin chip are all very successful SNN hard...

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Problems solved by technology

However, these structures do not reasonably apply the STDP rule to the training phase of SNN. Here, an all-digital SNN system is proposed, which organically combines the rational analysis of the STDP rule, and realizes the SNN through online training. Identification of the MNIST dataset

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