Method and hardware structure capable of realizing convolution calculation in multiple neural networks

Abstract

The invention provides a method and a hardware structure capable of realizing convolution calculation in various neural networks, which are characterized in that convolution calculation processes in various neural networks are analyzed, common characteristics of convolution calculation change modes involved in the convolution calculation processes are extracted, parameterization is carried out on the common characteristics. The configured and processed parameters are analyzed by using a hardware structure to realize analysis of different convolution algorithms. According to the method and the hardware structure capable of realizing the convolution calculation in the multiple neural networks, through abstracting multiple convolution neural network calculation processes, network changes are written into a register instruction capable of being analyzed by hardware, and the calculation state of the neural network calculation unit is sensed through the main control CPU; the main control CPU configures weight data, feature map data, a configuration register and other configuration information required by the convolutional neural network calculation unit in real time, and such a hardware structure can realize effective support for various convolutional neural network calculations, and is fast and compatible, and the calculation efficiency is not affected.

Description

technical field [0001] The invention belongs to the technical field of delay performance testing of switching chips, and in particular relates to a method and a hardware structure capable of realizing convolution calculation in various neural networks. Background technique [0002] At present, in the CNN (convolutional neural network) inference acceleration network hardware design, there are mainly the following design directions in the design, lightweight hardware design, low-power hardware design, sparse network hardware design, storage and calculation integrated hardware Design, large computing power hardware design and other directions, each hardware design method has different application scenarios, due to the calculation process of convolutional neural network, the hardware structure is relatively fixed for a certain network, but I want to design a relatively general hardware Environment, to support a variety of convolutional neural network structures, has become relat...

AI Technical Summary

Problems solved by technology

[0006] 1. The hardware environment is almost customized, and the support for network upgrades and network changes is poor;

[0007] 2. After the algorithm is updated and the scene changes, the hardware iteration cycle is longer, and the cost is high, and the tape-out risk is greater;

[0008] 3. Traditional CPU has low computing efficiency;

[0009] 4. The energy efficiency ratio of GPU is low, and the deployment cost is huge.

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