High performance compressive sensing reconstruction hardware with QRD process

Citations Over TimeTop 10% of 2012 papers

Abstract

This paper presents a high performance architecture for the reconstruction of compressive sampled signals using Orthogonal Matching Pursuit (OMP) algorithm. Q-R decomposition (QRD) process is used for the matrix inverse core and a new algorithm for finding fast inverse square root of a fixed point number is also implemented to support the QRD process. The optimized architecture takes 256-length input vector and 64 measurement data, and reconstructs a signal of sparsity 8. The design is implemented in 65 nm CMOS which runs at 165 MHz and occupies 0.69 mm 2 , total reconstruction takes 13.7 μs. The implementation on Xilinx FPGA Virtex-5 takes 27.12 μs to reconstruct a 256-length signal of sparsity 8. The same architecture for 128-length signal of sparsity 5 on Virtex-5 is 2.4 times faster than the state-of-the-art implementation.

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