A single-precision compressive sensing signal reconstruction engine on FPGAs

Abstract

Compressive sensing (CS) is a promising technology for the low-power and cost-effective data acquisition in wireless healthcare systems. However, its efficient realtime signal reconstruction is still challenging, and there is a clear demand for hardware acceleration. In this paper, we present the first single-precision floating-point CS reconstruction engine implemented a Kintex-7 FPGA using the orthogonal matching pursuit (OMP) algorithm. In order to achieve high performance with maximum hardware utilization, we propose a highly parallel architecture that shares the computing resources among different tasks of OMP by using configurable processing elements (PEs). By fully utilizing the FPGA recourses, our implementation has 128 PEs in parallel and operates at 53.7 MHz. In addition, it can support 2x larger problem size and 10x more sparse coefficients than prior work, which enables higher reconstruction accuracy by adding finer details to the recovered signal. Hardware results from the ECG reconstruction tests show the same level of accuracy as the double-precision C program. Compared to the execution time of a 2.27 GHz CPU, the FPGA reconstruction achieves an average speed-up of 41x.