Universal Deep Neural Network Compression
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Compression of deep neural networks (DNNs) for memory- and computation-efficient compact feature representations becomes a critical problem particularly for deployment of DNNs on resource-limited platforms. In this paper, we investigate lossy compression of DNNs by weight quantization and lossless source coding for memory-efficient inference. Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding. In particular, we examine universal randomized lattice quantization of DNNs, which randomizes DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution. Entropy coding schemes such as Huffman codes require prior calculation of source statistics, which is computationally consuming. Instead, we propose universal lossless source coding schemes such as variants of Lempel-Ziv-Welch or the Burrows-Wheeler transform. Finally, we present the methods of fine-tuning vector quantized DNNs to recover the performance loss after quantization. Our experimental results show that the proposed universal DNN compression scheme achieves compression ratios of 124.80, 47.10 and 42.46 for LeNet5, 32-layer ResNet and AlexNet, respectively.
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