Hardware-efficient compressed sensing encoder designs for WBSNs

Implanted sensors, as might be used with wireless body sensor networks, must have minimal size and power consumption. In this work we examine digital-based compressed sensing encoders for WBSN-enabled ECG and EEG monitoring, a domain that has received much recent attention. We have two major findings. The first is that using a random binary Toeplitz matrix, rather than Bernoulli, has an acceptable effect on recovery quality. The second is that, in this design space, leakage dominates over dynamic power with the result that it is highly beneficial to reduce the number of accumulators to trade off space for operating frequency. We demonstrate these results with a parameterized design and over three application domains-EEG, ECG, and small images-which together represent a variety of recovery goals and therefore compression methods. Compared with previous implementations, our new design consumes 1-to-2 orders of magnitude less area and power while still meeting timing constraints and achieving comparable recovery quality.