Configurable universal convolutional neural network accelerator

Abstract

The invention discloses a configurable universal convolutional neural network accelerator, and belongs to the technical field of calculation, calculation and counting. The accelerator comprises a PE array, a state controller, a function module, a weight cache region, a feature map cache region, an output cache region and a register stack; the state controller comprises a network parameter registerand a working state controller. An excellent acceleration effect can be achieved for networks of different scales by configuring a network parameter register, and a working state controller controlsswitching of the working state of the accelerator and sends a control signal to other modules. The weight cache region, the feature map cache region and the output cache region are each composed of aplurality of data sub-cache regions and used for storing weight data, feature map data and calculation results. According to the method, proper data reuse modes, array sizes and the number of sub-cache regions can be configured according to different network characteristics, and the method is good in universality, low in power consumption and high in throughput.

Description

technical field [0001] The invention discloses a configurable universal convolutional neural network accelerator, which belongs to the technical field of calculation, calculation and counting. Background technique [0002] In recent years, deep neural networks have developed faster and been widely used, and have achieved remarkable results in text recognition, image recognition, target tracking, face detection and recognition and other application fields. The scale of the deep neural network continues to increase as the application scenarios become more complex, and a large number of parameters need to be stored and calculated. Therefore, how to accelerate and implement large-scale deep neural networks on hardware has become an important issue in the field of machine learning. [0003] GPU (Graphic Processing Unit, graphics processing unit) and multi-core CPU (Central Processing Unit, central processing unit) are commonly used devices to accelerate large-scale deep neural n...

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Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned background technology and provide a configurable general-purpose convolutional neural network accelerator. By configuring network parameters, the acceleration of convolutional neural network structures of various scales is realized, and neural networks with different structures Using different d

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