Title: Succinct Compression: Near-Optimal and Lossless Compression of Deep Neural Networks during Inference Runtime. Abstract: Recent advances in Deep Neural Networks (DNN) compression (e.g. pruning, quantization and etc.) significantly reduces the amount of space consumption for storage, making them easier to deploy in low-cost devices. However, those techniques do not keep the compressed representation during inference runtime, which incurs significant overheads in terms of both performance and space consumption. We introduce ``Succinct Compression”, a three-stage framework to enable DNN inference with near-optimal compression and much better performance during inference runtime. The key insight of our method leverages the concept of \textit{Succinct Data Structures}, which supports fast queries directly on compressed representation without decompression. Our method first transforms DNN models as our proposed formulations in either Element-wise or Block-wise manner, so that \textit{Succinct Data Structures} can take advantage of. Then, our method compresses transformed DNN models using \textit{Succinct Data Structures}. Finally, our method exploits our specialized execution pipelines for different model formulations, to retrieve relevant data for DNN inference. Our experimental results show that, our method keeps near-optimal compression, and achieves at least 8.7X/11.5X speedup on AlexNet/VGG-16 inference, compared with Huffman Coding. We also experimentally show that our method is quite synergistic with Pruning and Quantization.  
