Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

[1]  A. Robert Calderbank,et al.  Deterministic compressed sensing , 2011 .

[2]  Frederick T. Chen,et al.  Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM , 2008, 2008 IEEE International Electron Devices Meeting.

[3]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[4]  Tzyy-Ping Jung,et al.  Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware , 2012, IEEE Transactions on Biomedical Engineering.

[5]  Dejan Markovic,et al.  A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals , 2016, IEEE Journal of Solid-State Circuits.

[6]  Martin Vetterli,et al.  Randomized recovery for boolean compressed sensing , 2013, 2013 IEEE International Symposium on Information Theory.

[7]  Yang-Kyu Choi,et al.  Resistive switching of aluminum oxide for flexible memory , 2008 .

[8]  Norman P. Jouppi,et al.  CACTI: an enhanced cache access and cycle time model , 1996, IEEE J. Solid State Circuits.

[9]  Frederick T. Chen,et al.  Highly scalable hafnium oxide memory with improvements of resistive distribution and read disturb immunity , 2009, 2009 IEEE International Electron Devices Meeting (IEDM).

[10]  Sridhar Krishnan,et al.  Compressive Sensing of Electrocardiogram Signals by Promoting Sparsity on the Second-Order Difference and by Using Dictionary Learning , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[11]  Chinmay Hegde,et al.  NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings , 2015, IEEE Transactions on Signal Processing.

[12]  Vladimir Stojanovic,et al.  Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors , 2012, IEEE Journal of Solid-State Circuits.

[13]  Dejan Markovic,et al.  18.5 A configurable 12-to-237KS/s 12.8mW sparse-approximation engine for mobile ExG data aggregation , 2015, 2015 IEEE International Solid-State Circuits Conference - (ISSCC) Digest of Technical Papers.

[14]  Heng-Yuan Lee,et al.  A 5ns fast write multi-level non-volatile 1 K bits RRAM memory with advance write scheme , 2009, 2009 Symposium on VLSI Circuits.

[15]  Daibashish Gangopadhyay,et al.  Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[16]  Lino A. Costa,et al.  Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems , 2001 .

[17]  Nikolaos V. Sahinidis,et al.  BARON: A general purpose global optimization software package , 1996, J. Glob. Optim..

[18]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[19]  Wei Zhang,et al.  Nonvolatile CBRAM-Crossbar-Based 3-D-Integrated Hybrid Memory for Data Retention , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[20]  Dmitry M. Malioutov,et al.  Boolean compressed sensing: LP relaxation for group testing , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[22]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[23]  A. Robert Calderbank,et al.  Reed Muller Sensing Matrices and the LASSO , 2010, ArXiv.

[24]  Ralph Etienne-Cummings,et al.  Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[25]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[26]  Xin Li,et al.  Optimizing Boolean embedding matrix for compressive sensing in RRAM crossbar , 2015, 2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).

[27]  R. DeVore,et al.  A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .