Hardware structure for realizing forward calculation of convolutional neural network

Abstract

The present application discloses a hardware structure for realizing forward calculation of a convolutional neural network. The hardware structure comprises: a data off-chip caching module, used for caching parameter data in each to-be-processed picture that is input externally into the module, wherein the parameter data waits for being read by a multi-level pipeline acceleration module; the multi-level pipeline acceleration module, connected to the data off-chip caching module and used for reading a parameter from the data off-chip caching module, so as to realize core calculation of a convolutional neural network; a parameter reading arbitration module, connected to the multi-level pipeline acceleration module and used for processing multiple parameter reading requests in the multi-level pipeline acceleration module, so as for the multi-level pipeline acceleration module to obtain a required parameter; and a parameter off-chip caching module, connected to the parameter reading arbitration module and used for storing a parameter required for forward calculation of the convolutional neural network. The present application realizes algorithms by adopting a hardware architecture in a parallel pipeline manner, so that higher resource utilization and higher performance are achieved.

Description

technical field [0001] The present application relates to the field of computer and electronic information technology, and in particular to a hardware structure for realizing forward calculation of convolutional neural network. Background technique [0002] With the rise of artificial intelligence, deep learning has become a very popular field. It is widely used in computer vision, speech recognition and other big data applications, and has received more and more attention. As a very important algorithm model in deep learning, convolutional neural network has been widely used in image classification, face recognition, video detection, speech recognition and so on. The convolutional neural network is modeled on the nervous system in the human brain. It consists of many layers. The input information is passed from the initial input layer to the next layer through some convolution, multiplication and addition operations, activation functions, etc., to This method is passed lay...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a hardware structure for realizing the forward calculation of the convolutional neural network, which is used to solve the problem that the hardware acceleration of the convolutional neural network in the prior art cannot effectively and fully utilize the resources on the board for maximum parallel acceleration. Issues that reduce resource utilization and performance

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