A 232-to-1996KS/s robust compressive-sensing reconstruction engine for real-time physiological signals monitoring

Compressive sensing (CS) techniques enable new reduced-complexity designs for sensor nodes and help reduce overall transmission power in wireless sensor network [1-2]. Prior CS reconstruction chip designs have been described in [3-4]. However, for real-time monitoring of physiological signals, the applied orthogonal matching pursuit (OMP) algorithms they incorporate are sensitive to measurement noise interference and suffer from a slow convergence rate. This paper presents a new CS reconstruction engine fabricated in 40nm CMOS with following features: 1) A sparsity-estimation framework to suppress measurement noise interference at sensing nodes, achieving at least 8dB signal-to-noise ratio (SNR) gain under the same success rate for robust reconstruction. 2) A new flexible indices-updating VLSI architecture, inspired by the gradient descent method [5], that can support arbitrary signal dimension, (Lnew, M), of CS reconstruction with high sparsity level (Kmax). 3) Parallel-searching, indices-bypassing, and functional blocks that automatically group processing elements (PEs) are designed to reduce the total CS reconstruction cycle latency by 84%. Compared with prior state-of-the-art designs, this CS reconstruction engine can achieve 10x higher throughput rate and 4.2x better energy efficiency at the minimum-energy point (MEP).