Low Overhead CS-Based Heterogeneous Framework for Big Data Acceleration

Abstract

Big data processing on hardware gained immense interest among the hardware research community to take advantage of fast processing and reconfigurability. Though the computation latency can be reduced using hardware, big data processing cost is dominated by data transfers. In this article, we propose a low overhead framework based on compressive sensing (CS) to reduce data transfers up to 67% without affecting signal quality. CS has two important kernels: “sensing” and “reconstruction.” In this article, we focus on CS reconstruction is using orthogonal matching pursuit (OMP) algorithm. We implement the OMP CS reconstruction algorithm on a domain-specific PENC many-core platform and a low-power Jetson TK1 platform consisting of an ARM CPU and a K1 GPU. Detailed performance analysis of OMP algorithm on each platform suggests that the PENC many-core platform has 15× and 18× less energy consumption and 16× and 8× faster reconstruction time as compared to the low-power ARM CPU and K1 GPU, respectively. Furthermore, we implement the proposed CS-based framework on heterogeneous architecture, in which the PENC many-core architecture is used as an “accelerator” and processing is performed on the ARM CPU platform. For demonstration, we integrate the proposed CS-based framework with a hadoop MapReduce platform for a face detection application. The results show that the proposed CS-based framework with the PENC many-core as an accelerator achieves a 26.15% data storage/transfer reduction, with an execution time and energy consumption overhead of 3.7% and 0.002%, respectively, for 5,000 image transfers. Compared to the CS-based framework implementation on the low-power Jetson TK1 ARM CPU+GPU platform, the PENC many-core implementation is 2.3× faster for the image reconstruction part, while achieving 29% higher performance and 34% better energy efficiency for the complete face detection application on the Hadoop MapReduce platform.