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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter, comprising: 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. 2. The method according to claim 1, wherein, the deep convolutional neural network is of a plurality of convolutional layers, the quantizing a parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of books comprises: repeatedly executing the following processes until the plurality of convolutional layers have all been traversed: dividing a parameter of the convolutional layers into M groups, each group comprising a plurality of first parameter sub-vectors, wherein, M is a positive integer; performing a K-means clustering for the plurality of parameter sub-vectors of each group to obtain a corresponding first subcode book of each group, the first subcode book comprising a plurality of first code words; calculating a Euclidean distance between the plurality of first parameter sub-vectors and the plurality of first code words, respectively; acquiring a first parameter sub-vector and a first code word with the minimum of the Euclidean distance, and recording an index value of the first code word with the minimum of Euclidean distance. 3. The method according to claim 2, wherein, the deep convolutional neural network is of a plurality of fully connected layers, the quantizing a 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 comprises: repeatedly executing the following processes until the plurality of fully connected layers have all been traversed: dividing a parameter of the fully connected layers into M groups, each group comprising a plurality of second parameter sub-vectors; performing a K-means clustering for the plurality of second parameter sub-vectors respectively to obtain a corresponding second subcode book of each of the groups, the second subcode book comprising a plurality of second code words; calculating a Euclidean distance between the plurality of second parameter sub-vectors and the plurality of second code words, respectively; acquiring a second parameter sub-vector and a second code word with the minimum of the Euclidean distance, and recording an index value of the second code word with the minimum of Euclidean distance. 4. The method according to claim 3, wherein, the deep convolutional neural network is of the plurality of convolutional layers, the 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 comprises: repeatedly executing the following processes until the plurality of convolutional layers have all been traversed: inputting a first feature map; dividing the first feature map into M groups, each of the groups comprising a plurality of first feature sub-vectors; performing an inner product operation between the plurality of first feature sub-vectors and the plurality of first code words in a corresponding first subcode book of each group respectively to obtain a plurality of first operation results, and storing the plurality of the first operation results in a first lookup table; summing the plurality of first operation results to obtain an output first feature map. 5. The method according to claim 4, wherein, the deep convolutional neural network is of the plurality of fully connected layers, the acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and the respective corresponding index values of the plurality of subcode books comprises: repeatedly executing the following processes until the plurality of fully connected layers have all been traversed: inputting a second feature map; dividing the second feature map into M groups, each of the groups comprising a second feature sub-vector; performing an inner product operation between the second feature sub-vector and the plurality of second code words in a corresponding second subcode book of each group to obtain a plurality of second operation results, and storing the plurality of second operation results in a second lookup table; summing the plurality of second operation results to obtain an output second feature map.