Processing system and method for binary weight convolution neural network

Abstract

The invention provides a processing system for a binary weight convolution neural network. The system comprises at least one storage unit for storing data and an instruction, at least one control unit for obtaining the instruction stored in the storage unit and sending out a control signal, and at least one calculation unit for obtaining one layer of node value in a convolution neural network and corresponding binary weight value data from the storage unit and performing adding and subtraction operations to obtain a next layer of node value. Therefore, the data bit width during convolution neural network calculation process can be reduced; the convolution operation speed can be increased; and the storage capacity and working energy consumption can be reduced.

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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