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A kind of convolutional neural network cnn hardware accelerator and acceleration method

A convolutional neural network and hardware accelerator technology, applied in the field of deep learning hardware acceleration, can solve the problems of reduced accelerator efficiency, poor scalability of systolic arrays, and high idle rate of computing units, so as to improve the reuse rate, reduce the amount of data moving, and improve the The effect of computing performance

Active Publication Date: 2020-12-22
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0005] 1) When the pulsating array structure is adopted, the computing unit (PE) is prone to a high idle rate, which in turn reduces the efficiency of the accelerator;
[0006] 2) When the systolic array structure is used, the scalability of the systolic array is poor due to the proportional increase in bandwidth required to maintain the required acceleration factor

Method used

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  • A kind of convolutional neural network cnn hardware accelerator and acceleration method
  • A kind of convolutional neural network cnn hardware accelerator and acceleration method
  • A kind of convolutional neural network cnn hardware accelerator and acceleration method

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0044] Such as figure 1As shown, the convolutional neural network CNN hardware accelerator in this embodiment includes an input buffer 1 for caching input feature picture data and a plurality of computing units 2 (PE) that share the same input feature picture data for CNN convolution operations, each The computing unit 2 includes a convolution kernel buffer 21, an output buffer 22, and a multiply-add unit 23 composed of multiple MAC components; the CNN hardware accelerator is connected to an external storage component, and the external storage component provides the CNN hardware accelerator with computing data information and result write-back space . The convolution kernel buffer 21 receives the convolution kernel data returned from the external storage unit, ...

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Abstract

The invention discloses a convolution neural network (CNN) hardware accelerator and an acceleration method. The accelerator comprises an input buffer and a plurality of operation units , and is characterized in that the input buffer is used for caching input feature picture data, the plurality of operation units respectively share the input feature picture data to perform a CNN convolution operation, each operation unit comprises a convolution kernel buffer, an output buffer and a multiplier-adder unit formed by a plurality of MAC components, the convolution kernel buffer receives convolutionkernel data returned from an external storage component, the convolution kernel data is provided for each MAC component of the multiplier-adder unit, each MAC component receives the input feature picture data and the convolution kernel data to perform a multiply accumulation operation, and an intermediate result of the operation is written into the output buffer. The acceleration method is a method applying the accelerator. The CNN hardware accelerator and the acceleration method can improve the CNN hardware acceleration performance, and have the advantages of high data reuse rate and efficiency, small amount of data migration, good expansibility, small bandwidth required by the system, small hardware overhead and the like.

Description

technical field [0001] The present invention relates to the technical field of deep learning (Deep Learning, DL) hardware acceleration, in particular to a CNN (Convolution Neural Networks, Convolution Neural Networks) hardware accelerator and an acceleration method. Background technique [0002] Convolutional neural network (CNN) is one of the most important algorithms in deep learning. It is widely used in many fields such as target recognition, unmanned driving and artificial intelligence because of its high precision and small weight. In each network layer of convolutional neural network CNN, the convolutional layer accounts for more than 90% of the calculation amount and computing time of the entire network. Accelerating the operation of the convolutional layer is the key to improving the performance of CNN. Therefore, it is urgent to design a CNN hardware accelerator to Improve the efficiency of convolution operations. [0003] The CNN convolutional layer algorithm has...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T1/20G06N3/063
CPCG06N3/063G06T1/20
Inventor 刘胜郭阳陈胜刚万江华雷元武谭弘兵宋蕊曾国钊
Owner NAT UNIV OF DEFENSE TECH
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