Load-balanced sparse convolutional neural network accelerator and acceleration method thereof

A convolutional neural network and load balancing technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of high idle rate of computing resources, increase ineffective computing, etc., to meet the requirements of low power consumption and high energy efficiency ratio , to ensure the effect of high reuse rate, good applicability and scalability

Pending Publication Date: 2019-07-09
南京吉相传感成像技术研究院有限公司
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[0004] Due to the irregular distribution of non-zero elements in the sparse convolutional neural network, invali

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  • Load-balanced sparse convolutional neural network accelerator and acceleration method thereof
  • Load-balanced sparse convolutional neural network accelerator and acceleration method thereof
  • Load-balanced sparse convolutional neural network accelerator and acceleration method thereof

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[0024] The solution of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] Such as figure 1 The flow chart of the sparse convolutional network operation method for load balancing is shown. First, the weight data of the convolutional neural network model will be pruned, and the data will be grouped according to the scale parameters of the weight data. Then, on the basis of ensuring the overall accuracy of the model The same pruning method is used for each group of weight data for sparse processing; then a load-balanced sparse convolution operation mapping scheme is formulated according to the input feature map of the convolution operation and the size of the convolution kernel, and the sparse convolution neural network is mapped to to the PE (Process Element Computing Unit) array of the convolution operation of the hardware accelerator; then the hardware accelerator reconstructs the PE array and the storage array accor...

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Abstract

The invention discloses a load-balanced sparse convolutional neural network accelerator and an acceleration method thereof. The accelerator comprises a main controller, a data distribution module, a convolution operation calculation array, an output result caching module, a linear activation function unit, a pooling unit, an online coding unit and an off-chip dynamic memory. According to the scheme provided by the invention, the high-efficiency operation of the convolution operation calculation array can be realized under the condition of few storage resources, the high multiplexing rate of input excitation and weight data is ensured, and the load balance and high utilization rate of the calculation array are ensured; meanwhile, the calculation array supports convolution operations of different sizes and different scales and parallel scheduling of two layers between rows and columns and between different feature maps through a static configuration mode, and the method has very good applicability and expansibility.

Description

technical field [0001] The invention relates to a load-balanced sparse convolutional neural network accelerator and an acceleration method thereof, belonging to the technical field of deep learning algorithms. Background technique [0002] In recent years, deep learning algorithms have been widely used and achieved excellent results in computer vision, natural language processing and speech recognition, and convolutional neural network (CNN) is one of the most important algorithms. The higher accuracy of the convolutional neural network model often means deeper network layers, as well as more network parameters and calculations, of which 90% of the calculations are concentrated on the convolutional layer, so in order to better in the embedded To run convolutional neural networks efficiently on the system, it is imperative to optimize the energy efficiency ratio of convolution operations. [0003] There are two main characteristics of convolutional neural network CNN convolu...

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Application Information

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IPC IPC(8): G06N3/063G06N3/04G06N3/08
CPCG06N3/063G06N3/082G06N3/045
Inventor 王瑶朱志炜秦子迪苏岩王宇宣
Owner 南京吉相传感成像技术研究院有限公司
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