Processing system and method applied to binary weight convolution network

Abstract

The invention provides a processing system applied to a binary weight convolutional neural network. The system includes: at least one storage unit for storing data and instructions; at least one control unit for obtaining instructions stored in the storage unit and sending control signals; at least one computing unit for obtaining from the storage unit The node value of one layer in the convolutional neural network and the corresponding binary weight value data are obtained by performing addition and subtraction operations to obtain the node value of the next layer. The system of the invention reduces the data bit width in the calculation process of the convolutional neural network, improves the convolution operation speed, and reduces the storage capacity and work energy consumption.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a processing system and method applied to a binary weight convolution network. Background technique [0002] Deep learning technology has developed rapidly in recent years. Deep neural networks, especially convolutional neural networks, have made great achievements in image recognition, speech recognition, natural language understanding, weather prediction, gene expression, content recommendation and intelligent robots. Wide range of applications. The deep network structure obtained through deep learning is an operational model, which contains a large number of data nodes, each data node is connected to other data nodes, and the connection relationship between each node is represented by weight. With the continuous improvement of the complexity of the neural network, the neural network technology has many problems in the actual application process, such as occupying a lot of r...

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Problems solved by technology

[0004] However, most of the current deep learning applications are implemented using central processing units and graphics processing units. These technologies are not energy efficient, and there are serious energy efficiency problems and computing speed bottlenecks when applied in embedded devices or low-overhead data centers. , it is difficult to meet the performance requirements of the application, so it is difficult to apply it to small and light-weight devices such as mobile phones and embedded electronic devices

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