ZCOMP

Abstract

Deep Neural Networks (DNNs) are becoming the prevalent approach in computer vision, machine learning, natural language processing, and speech recognition applications. Although DNNs are perceived as compute-intensive tasks, they also apply intense pressure on the capacity and bandwidth of the memory hierarchy, primarily due to the large intermediate data communicated across network layers. Prior work on hardware DNN accelerators leverages the cross-layer data sparsity via fully-customized datapaths. However, dynamically compressing/expanding such data is a challenging task for general-purpose multi-processors with virtual memory and hardware-managed coherent cache hierarchies.