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In this work, we propose an effective scheme (called DP-Net) for compressingthe deep neural networks (DNNs). It includes a novel dynamic programming (DP)based algorithm to obtain the optimal solution of weight quantization and anoptimization process to train a clustering-friendly DNN. Experiments showedthat the DP-Net allows larger compression than the state-of-the-artcounterparts while preserving accuracy. The largest 77X compression ratio onWide ResNet is achieved by combining DP-Net with other compression techniques.Furthermore, the DP-Net is extended for compressing a robust DNN model withnegligible accuracy loss. At last, a custom accelerator is designed on FPGA tospeed up the inference computation with DP-Net.

DP-Net: Dynamic Programming Guided Deep Neural Network Compression