Neural network information conversion method and system

Abstract

The invention relates to a neural network information conversion method and system. The method comprises the steps that neuron input information input by a presynaptic neuron is received, wherein artificial neuron input information input by a presynaptic artificial neuron or spiking neuron input information input by the presynaptic spiking neuron is included; according to the artificial neuron input information input by the presynaptic artificial neuron, through a preset artificial information conversion algorithm, the artificial neuron input information is converted to spiking neuron conversion information; or according to the spiking neuron input information, through a preset spiking information conversion algorithm, the spiking neuron input information is converted to artificial neuron conversion information; the spiking neuron conversion information or the artificial neuron conversion information is output. According to the neural network information conversion method and system, in one neural network, two different neuron information modes are compatible, and the information processing ability of the neural network is improved.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a neural network information conversion method and system. Background technique [0002] Most of today's artificial neural network research is still implemented in von Neumann computer software and high-performance GPGPU (General Purpose Graphic Processing Units) platform. The hardware overhead, energy consumption and information of the whole process The processing speed is not optimistic. For this reason, the field of neuromorphic computing has developed rapidly in recent years, that is, using hardware circuits to directly construct neural networks to simulate the functions of the brain, trying to achieve a computing platform that is massively parallel, low-energy, and capable of supporting complex pattern learning. [0003] However, in the traditional neuromorphic system, there are two main forms of neural network, one is spiking neural network and the other is artific...

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Problems solved by technology

[0003] However, in traditional neuromorphic systems, there are mainly two forms of neural networks, one is spiking neural network and the other is artificial neural network. The two have different expressions for the same input information, resulting in artificial neural network and spiking Neural networks are not compatible due to the different information they process

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