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.

中文翻译：

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车道压缩

本文介绍了 Lane Compression，这是一种用于机器学习的轻量级无损压缩技术，它基于对机器学习数据统计特性的详细研究。所提出的技术描述了在运行时间之前收集的机器学习数据，并将值按位划分为不同的车道具有更显着的统计特征。然后从少数低成本压缩技术中为每个通道选择最合适的压缩技术。Lane Compression 的计算和内存要求非常低，但它实现了与 Huffman 编码相当或更好的压缩率。我们在广泛的机器学习网络上评估和分析车道压缩，用于推理和重新训练。我们还演示了运行时之前的分析以及基于分析配置硬件的能力，从而保证了跨不同模型和数据集的稳健性能。描述了硬件实现，该方案的简单性使其适用于压缩片上和片外流量。