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