Neuron hardware structure and method of simulating pulse neural network by adopting neuron hardware structure

Abstract

The invention discloses a neuron hardware structure and a method of simulating a pulse neural network by adopting the neuron hardware structure. The neuron hardware structure is characterized in that a neural network is provided, and comprises a plurality of neuron layers, which comprise a plurality of neurons; every neuron comprises a synapse layer, which comprises a plurality of synapses. The above mentioned method is characterized in that 1) the synapse model can be determined; 2) the synapses can be simulated; 3) the neurons can be simulated; 4) the neuron layers can be simulated; 5) the neural network can be simulated. By adopting the neuron hardware structure, the hardware resources occupied by the single neuron node can be reduced. By adopting the neuron hardware structure to simulate the pulse neural network, the simulation time is short, the expandability is good, the hardware resources occupied by the pulse neural network can be reduced, and then the capability of the hardware device accommodating the neurons can be improved.

Description

technical field [0001] The invention relates to a large-scale pulse neural network technology, in particular to a method for simulating a pulse neural network with a neuron hardware structure. Background technique [0002] The rapid development of neuroscience has accumulated a lot of knowledge about the structure and function of the human brain. Studies have shown that the brain is composed of dense, complex interconnections of neurons, which exhibit many amazing properties, such as pattern recognition and decision-making control. The current understanding of biological neurons is that they transmit information and perform computations through the timing of spikes. The researchers proposed a computational model of spiking neural networks, which simulates behaviors such as information transmission between neurons and signal processing within neurons. At present, there are many fields using computing methods based on spiking neural networks, such as prediction, image proces...

AI Technical Summary

Problems solved by technology

Software modeling and simulating spiking neural networks are relatively easy to implement and have a short development cycle, but software is generally based on von Neumann's serial execution architecture, so for large-scale neural networks, software simulation requires a lot of simulation time and can be poor scalability

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