Lane Compression

Abstract

This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a compression rate comparable to or better than Huffman coding. We evaluate and analyse Lane Compression on a wide range of machine learning networks for both inference and re-training. We also demonstrate the profiling prior to run-time and the ability to configure the hardware based on the profiling guarantee robust performance across different models and datasets. Hardware implementations are described and the scheme’s simplicity makes it suitable for compressing both on-chip and off-chip traffic.