Mixed-domain analog frontend circuit design for power-efficient multi-channel sensor systems : (Invited Paper)

The number of channels in recent sensor systems, such as neural recorders and imagers, has been increasing exponentially, and their data sizes have necessitated wider bandwidth for data transmission, resulting in larger power consumption for communication. Although data compression is useful to overcome this issue and is commonly implemented after analog-to-digital converters (ADCs), some issues still remain, namely the increased analog frontend (AFE) circuits, which are proportional to the required channel counts, and requirements of higher throughput in the digital data compression circuit. We propose a mixed-domain AFE circuit design approach that has the potential to achieve a small area and low-power consumption for such multichannel sensor systems. We introduce two design examples: a time-domain analog compressed sensing (CS) encoder and a stochastic-domain discrete cosine transform (DCT) processor. The measurement and simulation results demonstrate their effectiveness in terms of power and area efficiency.

[1]  M. Ishida,et al.  Low-power output-capacitorless low-dropout regulator with adjustable charge injection technique for on–off-keying transmitters , 2014 .

[2]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[3]  Jan M. Rabaey,et al.  A 4.78 mm 2 Fully-Integrated Neuromodulation SoC Combining 64 Acquisition Channels With Digital Compression and Simultaneous Dual Stimulation , 2015, IEEE Journal of Solid-State Circuits.

[4]  Michael Elad,et al.  A Deep Learning Approach to Block-based Compressed Sensing of Images , 2016, ArXiv.

[5]  Ippei Akita,et al.  A current noise reduction technique in chopper instrumentation amplifier for high-impedance sensors , 2015, IEICE Electron. Express.

[6]  Abbas El Gamal,et al.  CMOS Image Sensor With Per-Column ΣΔ ADC and Programmable Compressed Sensing , 2013, IEEE Journal of Solid-State Circuits.

[7]  Brian M. Sadler,et al.  A Sub-Nyquist Rate Sampling Receiver Exploiting Compressive Sensing , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[8]  Ippei Akita,et al.  A 27-nV/√Hz 0.015-mm2 three-stage operational amplifier with split active-feedback compensation , 2013, 2013 IEEE Asian Solid-State Circuits Conference (A-SSCC).

[9]  John P. Hayes,et al.  Survey of Stochastic Computing , 2013, TECS.

[10]  Jan Van der Spiegel,et al.  Design of a low-noise, high power efficiency neural recording front-end with an integrated real-time compressed sensing unit , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[11]  Masatoshi Ishikawa,et al.  4.9 A 1ms high-speed vision chip with 3D-stacked 140GOPS column-parallel PEs for spatio-temporal image processing , 2017, 2017 IEEE International Solid-State Circuits Conference (ISSCC).

[12]  Ippei Akita,et al.  A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording , 2018, Sensors.

[13]  Riccardo Rovatti,et al.  Rakeness-Based Design of Low-Complexity Compressed Sensing , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  M. Komatsu,et al.  Fabrication of a low leakage current type impedance sensor to monitor soil water content for slope failure prognostics , 2017, 2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[15]  M. Komatsu,et al.  Fabrication of a low leakage current type impedance sensor with shielding structures to detect a low water content of soil for slope failure prognostics , 2018 .

[16]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[17]  Brian R. Gaines,et al.  Stochastic Computing Systems , 1969 .

[18]  Jan M. Rabaey,et al.  A 4.78mm2 fully-integrated neuromodulation SoC combining 64 acquisition channels with digital compression and simultaneous dual stimulation , 2014, VLSIC.

[19]  Ippei Akita,et al.  A chopper-stabilized instrumentation amplifier using area-efficient self-trimming technique , 2014 .

[20]  D. Tank,et al.  Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice , 2007, Neuron.

[21]  M. Ishida,et al.  High-gain on-chip antenna using a sapphire substrate for implantable wireless medical systems , 2014 .

[22]  Vladimir Stojanovic,et al.  Energy-Aware Design of Compressed Sensing Systems for Wireless Sensors Under Performance and Reliability Constraints , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[23]  Pei-Yun Tsai,et al.  Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[24]  Stanislav Herwik,et al.  A Wireless Multi-Channel Recording System for Freely Behaving Mice and Rats , 2011, PloS one.

[25]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[26]  Jan Van der Spiegel,et al.  A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[27]  Mikhail A. Lebedev,et al.  Chronic, Wireless Recordings of Large Scale Brain Activity in Freely Moving Rhesus Monkeys , 2014, Nature Methods.

[28]  Jan M. Rabaey,et al.  A Minimally Invasive 64-Channel Wireless μECoG Implant , 2015, IEEE Journal of Solid-State Circuits.

[29]  Ippei Akita,et al.  A Dynamic Latched Comparator Using Area-Efficient Stochastic Offset Voltage Detection Technique , 2018, IEICE Trans. Electron..

[30]  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.

[31]  Akira Matsuzawa,et al.  A CMOS image sensor with analog two-dimensional DCT-based compression circuits for one-chip cameras , 1997, IEEE J. Solid State Circuits.

[32]  Vertically aligned extracellular microprobe arrays/(111) integrated with (100)-silicon mosfet amplifiers , 2015, 2015 28th IEEE International Conference on Micro Electro Mechanical Systems (MEMS).

[33]  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.

[34]  Chih-Wen Lu,et al.  Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation , 2017, Sensors.

[35]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[36]  Omid Salehi-Abari,et al.  Why Analog-to-Information Converters Suffer in High-Bandwidth Sparse Signal Applications , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[37]  Ippei Akita,et al.  A 0.06mm2 14nV/√Hz chopper instrumentation amplifier with automatic differential-pair matching , 2013, 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[38]  Shida Sayaka,et al.  A 1ms High-Speed Vision Chip with 3D-Stacked 140GOPS Column-Parallel PEs for Spatio-Temporal Image Processing , 2017 .

[39]  Takeshi Kawano,et al.  Co-Design Method and Wafer-Level Packaging Technique of Thin-Film Flexible Antenna and Silicon CMOS Rectifier Chips for Wireless-Powered Neural Interface Systems , 2015, Sensors.

[40]  An-Yeu Wu,et al.  A 232–1996-kS/s Robust Compressive Sensing Reconstruction Engine for Real-Time Physiological Signals Monitoring , 2019, IEEE Journal of Solid-State Circuits.

[41]  Rui Paulo Martins,et al.  A reconfigurable low-noise dynamic comparator with offset calibration in 90nm CMOS , 2011, IEEE Asian Solid-State Circuits Conference 2011.

[42]  T. Kawano,et al.  A thin film flexible antenna with CMOS rectifier chip for RF-powered implantable neural interfaces , 2015, 2015 Transducers - 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[43]  M. Ishida,et al.  A low-noise small-area operational amplifier using split active-feedback compensation technique , 2018 .

[44]  Maysam Ghovanloo,et al.  A mm-sized free-floating wirelessly powered implantable optical stimulating system-on-a-chip , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).

[45]  Timothy Denison,et al.  Creating neural “co-processors” to explore treatments for neurological disorders , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).

[46]  Ippei Akita.  Development of low-power analog/RF mixed-signal circuits with flexible thin film devices for wireless BMI systems , 2015, 2015 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT).

[47]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[48]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[49]  Jie Han,et al.  Approximate computing: An emerging paradigm for energy-efficient design , 2013, 2013 18th IEEE European Test Symposium (ETS).

[50]  Naoya Onizawa,et al.  VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing , 2017, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[51]  Ippei Akita,et al.  A 181NW 970µG✓HZ Accelerometer Analog Front-End Employing Feedforward Noise Reduction Technique , 2018, 2018 IEEE Symposium on VLSI Circuits.

[52]  Sooyoung Chung,et al.  Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex , 2005, Nature.

[53]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

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

[55]  Ippei Akita,et al.  A digitally calibrated dynamic comparator using time-domain offset detection , 2014 .

[56]  Richard G. Baraniuk,et al.  Theory and Implementation of an Analog-to-Information Converter using Random Demodulation , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[57]  Kiyoung Choi,et al.  Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks , 2016, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[58]  Sanshiro Shishido,et al.  An 8K4K-resolution 60fps 450ke−-saturation-signal organic-photoconductive-film global-shutter CMOS image sensor with in-pixel noise canceller , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).