Customized deep neural network model compression method and system based on cloud edge cooperation
A deep neural network and compression system technology, applied in the field of customized deep neural network model compression, can solve problems such as insufficient memory size and computing power, limited edge scenarios, and inability to deploy real-time reasoning on the edge side
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[0084] The present invention evaluates the performance of the subclass distillation algorithm based on the CIFAR-10 data set. The CIFAR-10 dataset contains 50K training data of 32×32 size and 10K test data, including 10 categories in total. Based on the CIFAR-10 dataset, it is verified on the VGG classification model. The algorithm is implemented using the Pytorch deep learning framework. The model training runs on an Ubuntu server with two NVIDIA GTX 2080Ti GPUs. The learning rate of gradient descent SGD is 0.01, the momentum is set to 0.9, and the batchsize is set to 128.
[0085] The large model adopts the standard VGG-16. The structure of the compressed small model is obtained by cropping the channels of each convolutional layer of the standard VGG-16. That is, the number of channels of the 13 convolutional layers of the standard VGG-16 is [64, 64, 128, 128, 256, 256, 256, 512, 512, 512, 512, 512, 512].
[0086] The hyperparameters are set as: α=0.95 in formula 2, and t...
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