A New ACD-OMP Accelerator With Clustered Computing Look-Ahead

Abstract

The orthogonal matching pursuit (OMP) has been widely explored to realize real-time compressed sensing (CS) reconstruction. The matrix pseudo-inverse of the least squares (LSs) is the most computationally complex operation in the OMP. Among various algorithms to realize this complex operation, the alternative Cholesky decomposition (ACD) algorithm performs the best. However, it typically involves a very long computation time due to its iterative procedure. To accelerate the ACD-OMP algorithm, a novel method called clustered computing look-ahead (CCL) is proposed. Inspired by the famous parallel carry look-ahead adder (CLA), CCL adds a propagation matrix to decouple the data dependency in ACD and then uses a clustering operator to transform the iterative computation of ACD into a pipelined and parallelized computation. This brief also proposes an efficient hardware architecture of the CCL-based ACD-OMP algorithm for CS reconstruction. The proposed algorithm is implemented on field programmable gate array (FPGA). For sparse signals with the same sparsity and length, the proposed implementation is 1.96 times faster than state-of-the-art work.