A multi-layer data sub-area joint computing method for convolutional neural network acceleration

Abstract

The present invention provides a multi-layer data sub-area joint calculation method for convolutional neural network acceleration, which includes the following steps: Step 1, divide the first fully connected layer image into several first fully connected layer areas, reverse derivation For the input image data corresponding to the image data of each first fully connected layer region, the input image is divided into several input image regions corresponding to the first fully connected layer region according to the reverse derivation result; step 2, for each The input image regions are sequentially convolved until the corresponding first fully connected layer regions. In the process of performing convolution operations on the next input image region, the weights of the first fully connected layer region corresponding to the previous input image region Value data is read and manipulated. It reduces the bandwidth and storage required by the hardware system during operation, and balances the demand for hardware resources between the convolutional layer and the fully connected layer of the convolutional neural network.

Description

technical field [0001] The invention relates to a data flow scheduling technology for convolutional neural network operations, in particular to a multi-layer data sub-area joint calculation method for convolutional neural network acceleration. Background technique [0002] Deep learning is a research hotspot in machine learning in recent years, and excellent results have been achieved in many directions. Deep learning is now playing an increasingly important role in many disciplines. However, limited by hardware devices, the operation speed of deep learning is slower than that of traditional artificial neural computing networks and some other machine learning methods. Therefore, in some fields that require high-performance computing and computer computing, the acceleration of deep learning is particularly important. For hardware acceleration of deep learning algorithms, there are currently three types of implementation methods, including multi-core CPU, GPU, and FPGA. Thei...

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Problems solved by technology

Such characteristics will cause the following problems when hardware acceleration is performed: a large amount of computing resources are required to calculate the convolutional layer, and the data access bandwidth is not high; when the fully connected layer is calculated, high data access bandwidth is required, such an imbalance Will bring difficulties to hardware design

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