Pulse neural network simulation strategy based on GPU

Abstract

The invention discloses a pulse neural network simulation strategy based on a GPU. The pulse neural network simulation strategy comprises the following steps: initializing a neural network structure and a network weight; loading parameters and a network structure in the GPU, and creating a pulse queue; calculating a neuron membrane voltage according to the pulse distribution condition in the pulse queue; according to the membrane voltage value of the neuron and a threshold value, whether a pulse is emitted is judged; and the process from S3 to S5 is repeated until iteration is completed. According to the method, the simulation speed of the spiking neural network is accelerated, the advantages of GPU parallel computing and the characteristics of sparsity and concurrency of the spiking neural network are brought into full play, and meanwhile simulation of a larger-scale spiking neural network model is supported.

Description

technical field

[0001] The invention belongs to the fields of pulse neural network and high-performance computing, and in particular relates to the design and realization of a GPU-based pulse neural network simulation strategy. Background technique

[0002] With the rapid growth of labeled data and computing power, deep learning has been widely used in many fields, but training larger networks means more data, faster computing efficiency and higher energy consumption. In contrast, the human brain not only has a high level of intelligence but also consumes only about 25 watts of power. Therefore, as a new type of neural network, the bionic network represented by spiking neural network, which replaces real-valued input with discrete sequences, has received more and more attention.

[0003] From the perspective of bionics, the spiking neural network is more biologically interpretable because it operates in the form of real biological tissue. It refers to the biological learni...

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