Succinct Compression: Lossless Compression for Fast and Memory-Efficient Deep Neural Network Inference

Yicun Duan, Xiangjun Peng

Keywords: Succinct Data Structures, Deep Neural Networks, Efficient Inference

TL;DR: First work to introduce Succinct Data Structures for Fast and Memory-Efficient Computations of Deep Neural Networks

Abstract: This paper introduces ``Succinct Compression”, a method to provide lossless compression of Deep Neural Network (DNN) models for fast and memory-efficient inference. The key insight of our method leverages the concept of \textit{Succinct Data Structures}, which supports fast queries without decompressing the compressed representations. Our method consists of three new insights. First, we introduce two basic building blocks to formulate DNN models, and how they can be extended to be synergistic with compressed models (e.g. pruned or quantized models). Then, we propose a scheme to enable mixed-formulation inference for different layers, to better extract its benefits. Finally, our method exploits a specialized execution pipeline to incorporate different model formulations for fast inference. We quantitatively demonstrate that: our method can (1) enable faster and more memory-efficient inference on uncompressed models; (2) be synergistic with a variety of structure-altered/unaltered compression schemes with better speedup and compression ratio, while preserving the accuracy; and (3) can outperform all other state-of-the-art Model Coding approaches.

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