Neural network information conversion method and system

Abstract

The present invention relates to a neural network information conversion method and system. The method includes: receiving neuron input information input by a previous neuron, including receiving artificial neuron input information input by a previous artificial neuron, or receiving a previous pulse The input information of the pulse neuron input by the neuron; according to the input information of the artificial neuron input by the previous artificial neuron, the input information of the artificial neuron is converted into a pulse neuron by a preset artificial information conversion algorithm Convert information; or convert the input information of the pulse neuron into artificial neuron conversion information through a preset pulse information conversion algorithm according to the input information of the pulse neuron; output the conversion information of the pulse neuron or the artificial neuron Neurons transform information. The invention realizes the mode of simultaneously compatible with two different neuron information in one neural network, and improves the information processing capability of the neural network.

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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