Convolutional operation optimization method and system for efficiently running deep learning task

Abstract

The invention discloses a convolution operation optimization method and system for efficiently running a deep learning task. The method comprises the following steps: obtaining picture parameters andconvolution kernel parameters, segmenting the picture parameters and the convolution kernel parameters to obtain picture sub-tensors and convolution kernel sub-tensors, copying the segmented sub-tensors to a high-speed memory, performing convolution operation on the sub-tensors stored in an L1 cache, and assembling the sub-tensors subjected to convolution operation according to an assembling stepof a matrix partitioning algorithm to obtain a final result. Through matrixes and tensor partitioning strategies adjusted according to hardware parameters of different embedded platforms, in the wholeoperation process, more operation data can be obtained from a high-speed memory instead of low-speed storage, and the operation speed is increased; meanwhile, through a reasonable strategy for optimizing the assembly level of the embedded platform, the potential of the platform can be better utilized by operation, and the operation speed is further increased; in addition, a matrix partitioning strategy is adopted, so that the implementation cost is lower.

Description

technical field [0001] The invention relates to the technical field of computer performance optimization, in particular to a convolution operation optimization method and system for efficiently running deep learning tasks. Background technique [0002] At present, deep learning technology and Internet of Things technology are developing rapidly, and technologies that combine the two are also emerging, for example, using deep learning technology and Internet of Things technology for monitoring, using deep learning technology and Internet of Things technology for intelligent analysis Wait, it can be seen that the combination of deep learning technology and Internet of Things technology is becoming more and more popular among the public. [0003] However, limited by the computing power of embedded devices necessary for IoT technology, deep learning technology cannot achieve satisfactory results on these devices. The main disadvantages are as follows: 1. The output of the deep n...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a convolution operation optimization method and system for efficiently running deep learning tasks, which solves the problems in the prior art that the output of the deep neural network cannot be obtained within the time limit, the operating frequency of the CPU is slow, and the cost is high. The problem

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