Moffett International Co., Limited

Dongkuan Xu

Enxu Yan

Jiachao Liu

Embodiments disclosed herein allowed neural networks to be pruned. The inputs and outputs generated by a reference neural network are used to prune the reference neural network. The pruned neural network may have a subset of the weights that are in the reference neural network.

Neural network pruning method and system via layerwise analysis

Embodiments disclose bank-balanced-sparse activation neural network models and methods to generate the bank-balanced-sparse activation neural network models. According to one embodiment, a neural network sparsification engine determines a first deep neural network (DNN) model having two or more hidden layers. The engine determines a bank size, a bank layout, and a target sparsity. The engine segments the activation feature maps into a plurality of banks based on the bank size and the bank layout. The engine generates a second DNN model by increasing a sparsity for each bank of activation feature map based on the target sparsity, wherein the second DNN model is used for inferencing.

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Los Altos, CA, United States of America

Dongkuan Xu