Neuron information processing method and system for input weight expansion

Abstract

The invention relates to a neuron information processing method and a neuron information processing system with input weight expansion. The method comprises the steps of determining a preset number of continuous neurons as a cooperation set, determining a last neuron as an effective neuron, and determining the rest neuron as a cooperation neuron; acquiring transverse accumulated intermediate information of a first cooperation neuron by the first cooperation neuron in the cooperation set according to received frontend neuron information; successively acquiring the transverse accumulated intermediate information of the cooperation neurons by the subsequent cooperation neurons in the cooperation set, and determining the transverse accumulated intermediate information of the laser cooperation neuron as the transverse accumulated information; acquiring the cooperation output information and outputting the cooperation output information by the effective neuron according to the received frontend neuron information, the read current neuron information of the effective neuron, and the transverse accumulated information. The neuron information processing method and the neuron information processing system have advantages of increasing input weight types and improving information processing capability of a neural network.

Description

technical field [0001] The invention relates to the technical field of artificial neural networks, in particular to a neuron information processing method and system for expanding input weights. 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 a traditional neuromorphic system, the number of physical spaces of a single neuron weight memory is lim...

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Problems solved by technology

[0003] However, in a traditional neuromorphic system, the number of physical spaces of a single neuron weight memory is limited. When the number of input signals of a single neuron is greater than the number of physical spaces, the weight information corresponding to some input signals can only be Using the existing weights, for some neural networks that are sensitive to parameters, greatly affects the application performance of neurons

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