text: After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I O bandwidth and the energy consumption is dominated by I O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4× relative to uncompressed data and a gain of 60 over existing method can be achieved for ResNet-34 with a compression block requiring <300 bit of sequential cells and minimal combinational logic.
  @cite_27: As key applications become more data-intensive and the computational throughput of processors increases, the amount of data to be transferred in modern memory subsystems grows. Increasing physical bandwidth to keep up with the demand growth is challenging, however, due to strict area and energy limitations. This paper presents a novel and lightweight compression algorithm, Bit-Plane Compression (BPC), to increase the effective memory bandwidth. BPC aims at homogeneously-typed memory blocks, which are prevalent in many-core architectures, and applies a smart data transformation to both improve the inherent data compressibility and to reduce the complexity of compression hardware. We demonstrate that BPC provides superior compression ratios of 4.1:1 for integer benchmarks and reduces memory bandwidth requirements significantly.
summary: For this work, we build on a method known in the area of texture compression for GPUs, @cite_27 , fuse it with sparsity-focused compression methods, and evaluate the resulting compression algorithm on intermediate feature maps to show compression ratios of 4.4 and 2.8 for 8 ,bit AlexNet and SqueezeNet, respectively.