Low Overhead Architectures for OMP Compressive Sensing Reconstruction Algorithm

Abstract

Orthogonal Matching Pursuit (OMP) is an important compressive sensing (CS) recovery and sparsity inducing algorithm, which has potential in various emerging applications ranging from wearable and mobile computing to real-time analytics processing on servers. Thus application aware OMP algorithm implementation is important. In this paper, we propose two different modifications to OMP algorithm named Thresholding technique for OMP (tOMP) and Gradient Descent OMP (GDOMP) to reduce hardware complexity of OMP algorithm. tOMP modifies identification stage of OMP algorithm to reduce reconstruction time and GDOMP modifies residual update phase to reduce chip area. To demonstrate reconstruction efficiency of proposed OMP modifications, we compare signal-to-reconstruction error rate (SRER), signal-to-noise ratio (PSNR), and Structural Similarity index (SSIM) of previously proposed matching pursuit algorithms such as Subspace Pursuit (SP), Look Ahead OMP (LAOMP), and OMP, with tOMP, and GDOMP. We implemented reconfigurable, parallel, and pipelined architectures for three algorithms including OMP, tOMP, and GDOMP which can reconstruct different data vector sizes ranging from 128 to 1024, on 65 nm CMOS technology operating at 1 V supply voltage. The post place and route analysis on area, power, and latency show that, tOMP requires 33% less reconstruction time, and GDOMP consumes 44% less chip area when compared to OMP ASIC implementation. Compared to previously published work, the proposed architectures achieve 2.1 times improvement in Area-Delay product (ADP) and consume 40% less energy.