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Pruning is one of the major methods to compress deep neural networks. In thispaper, we propose an Ising energy model within an optimization framework forpruning convolutional kernels and hidden units. This model is designed toreduce redundancy between weight kernels and detect inactive kernels/hiddenunits. Our experiments using ResNets, AlexNet, and SqueezeNet on CIFAR-10 andCIFAR-100 datasets show that the proposed method on average can achieve apruning rate of more than $50\%$ of the trainable parameters with approximately$<10\%$ and $<5\%$ drop of Top-1 and Top-5 classification accuracy,respectively.

Pruning of Convolutional Neural Networks Using Ising Energy Model