Chinese Academy of Sciences

Alibaba Group Holding Limited

Cong Leng

Jian Cheng

Hanqing Lu

Jiaxiang Wu

Rong Jin

Hao Li

Zesheng Dou

Shenghuo Zhu

An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter provided by the present application comprises: quantizing the parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of subcode books; acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and respective corresponding index values of the plurality of subcode books. The present application may implement the acceleration and compression for a deep convolutional neural network.

Deep convolutional neural network acceleration and compression method based on parameter quantification

A method including receiving, by a processor, a computing instruction for a neural network, wherein the computing instruction for the neural network includes a computing rule for the neural network and a connection weight of the neural network, and the connection weight is a power of 2; and inputting, for a multiplication operation in the computing rule for the neural network, a source operand corresponding to the multiplication operation to a shift register, and performing a shift operation based on a connection weight corresponding to the multiplication operation, wherein the shift register outputs a target result operand as a result of the multiplication operation. The neural network uses a shift operation, and a neural network computing speed is increased.

Neural network computing

An image matching method based on cascaded binary encoding includes using a hashing look-up with multiple hashing tables to coarsely filter candidate key-points in an image to produce a candidate subset of key-points, projecting the candidate subset into a high-dimensional Hamming space, and building a 'Hamming distance-memory address' hashing table. An optimal matching key-point is discovered by querying the hashing table. The image matching method has high processing speed and matching quality, which can be used for efficient and accurate image matching.

Image matching method based on cascaded binary encoding

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Beijing, China

Cong Leng