Chinese Academy of Sciences

Haobo Xu

Ying Wang

Yinhe Han

Feng Min

The present invention provides a processing system for a binary weight convolutional neural network. The system comprises: at least one storage unit for storing data and instructions; at least one control unit for acquiring the instructions stored in the storage unit and sending out a control signal; and, at least one calculation unit for acquiring, from the storage unit, node values of a layer in a convolutional neural network and corresponding binary weight value data and obtaining node values of a next layer by performing addition and subtraction operations. With the system of the present invention, the data bit width during the calculation process of a convolutional neural network is reduced, the convolutional operation speed is improved, and the storage capacity and operational energy consumption are reduced.

Processing system and method for binary weight convolutional neural network

Disclosed are a weight data storage method and a convolution computation method that may be implemented in a neural network. The weight data storage method comprises searching for effective weights in a weight convolution kernel matrix and acquiring an index of effective weights. The effective weights are non-zero weights, and the index of effective weights is used to mark the position of the effective weights in the weight convolution kernel matrix. The weight data storage method further comprises storing the effective weights and the index of effective weights. According to the weight data storage method and the convolution computation method of the present disclosure, storage space can be saved, and computation efficiency can be improved.

Weight data storage method and neural network processor based on the method

The present invention relates to a weight management method and system for neural network processing. The method includes two stages, i.e., off-chip encryption stage and on-chip decryption stage: encrypting trained neural network weight data in advance, inputting the encrypted weight into a neural network processor chip, and decrypting the weight in real time by a decryption unit inside the neural network processor chip to perform related neural network calculation. The method and system realizes the protection of weight data without affecting the normal operation of a neural network processor.

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Beijing, China

Haobo Xu