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Conference Proceedings

B. Pérez-Sánchez, Fontenla-Romero, O. , and Guijarro-Berdiñas, B. , “A supervised learning method for neural networks based on sensitivity analysis with automatic regularization”, International Work-Conference on Artificial Neural Networks. Springer, pp. 157–164, 2009.

H. Palangi, Ward, R. K. , and Deng, L. , “Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors.”, ICASSP. pp. 3337–3341, 2013.

M. Rastegari, Ordonez, V. , Redmon, J. , and Farhadi, A. , “Xnor-net: Imagenet classification using binary convolutional neural networks”, European Conference on Computer Vision. Springer, pp. 525–542, 2016.

Journal Article

Y. I. Li, Liu, Z. , Liu, W. , Jiang, Y. , Wang, Y. , Goh, W. Ling, Yu, H. , and Ren, F. , “A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing”, IEEE Transactions on Industrial Electronics (TIE), vol. 66, no. 9, pp. 7407-7416, 2019.

(3.34 MB)

B. Hu, Ren, F. , Chen, Z. - Z. , Jiang, X. , and Chang, M. - C. Frank, “An 8-Bit Compressive Sensing ADC With 4GS/s Equivalent Speed Utilizing Self-Timed Pipeline SAR-Binary-Search”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 63, no. 10, pp. 934-938, 2016.

(1.8 MB)

B. Hu, Ren, F. , Chen, Z. - Z. , Jiang, X. , and Chang, M. - C. Frank, “9-bit time–digital-converter-assisted compressive-sensing analogue–digital-converter with 4 GS/s equivalent speed”, IET Electronics Letters, vol. 52, no. 6, pp. 430-432, 2016.

(511.29 KB)

M. Courbariaux, Hubara, I. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1”, arXiv preprint arXiv:1602.02830, 2016.

M. Kim and Smaragdis, P. , “Bitwise neural networks”, arXiv preprint arXiv:1601.06071, 2016.

Y. I. Li, Zhang, S. , Zhou, X. , and Ren, F. , “Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning”, Neurocomputing, vol. 398, pp. 45-54, 2020.

(1.91 MB)

G. Chen and Needell, D. , “Compressed sensing and dictionary learning”, Preprint, vol. 106, 2015.

S. Qaisar, Bilal, R. Muhammad, Iqbal, W. , Naureen, M. , and Lee, S. , “Compressive sensing: From theory to applications, a survey”, Journal of Communications and networks, vol. 15, pp. 443–456, 2013.

F. Ren and Marković, D. , “A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals”, IEEE Journal of Solid-State Circuits, vol. 51, no. 1, pp. 68-78, 2016.

(3.07 MB)

D. Needell and Tropp, J. A. , “Cosamp: iterative signal recovery from incomplete and inaccurate samples”, Communications of the ACM, vol. 53, pp. 93–100, 2010.

S. Nam, Davies, M. E. , Elad, M. , and Gribonval, R. , “The cosparse analysis model and algorithms”, Applied and Computational Harmonic Analysis, vol. 34, pp. 30–56, 2013.

Y. Wang, Li, X. , Xu, K. , Ren, F. , and Yu, H. , “Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing”, IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 2, pp. 255-266, 2017.

(2.53 MB)

Y. LeCun, Bengio, Y. , and Hinton, G. , “Deep learning”, Nature, vol. 521, pp. 436–444, 2015.

Y. Feng, Yang, F. , Zhou, X. , Guo, Y. , Tang, F. , Ren, F. , Guo, J. , and Ji, S. , “A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 6, pp. 1794-1801, 2019.

(1.73 MB)

J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural networks, vol. 61, pp. 85–117, 2015.

H. Palangi, Ward, R. K. , and Deng, L. , “Distributed Compressive Sensing: A Deep Learning Approach.”, IEEE Trans. Signal Processing, vol. 64, pp. 4504–4518, 2016.

D. L. Donoho and Tsaig, Y. , “Fast solution of $$\backslash$ell \_ $\$1$\$ $-norm minimization problems when the solution may be sparse”, IEEE Transactions on Information Theory, vol. 54, pp. 4789–4812, 2008.

Y. I. Li, Liu, Z. , Xu, K. , Yu, H. , and Ren, F. , “A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks”, ACM Journal on Emerging Technologies in Computing (JETC) - Special Issue on Frontiers of Hardware and Algorithms for On-chip Learning, vol. 14, no. 2, p. 18.16, 2018.

(1.92 MB)

M. A. T. Figueiredo, Nowak, R. D. , and Wright, S. J. , “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems”, IEEE Journal of selected topics in signal processing, vol. 1, pp. 586–597, 2007.

J. Li, Liang, J. , Li, L. , Ren, F. , Hu, W. , Li, J. , Qi, S. , and Pei, Q. , “Healable Capacitive Touch Screen Sensors Based on Transparent Composite Electrodes Comprising Silver Nanowires and a Furan/Maleimide Diels-Alder Cycloaddition Polymer”, ACS Nano, vol. 8, no. 12, pp. 12874–12882, 2014.

(6.99 MB)

W. Dally, “High-performance hardware for machine learning”, NIPS Tutorial, 2015.

M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , “Introduction to compressed sensing”, preprint, vol. 93, p. 2, 2011.

E. J. Candès and Wakin, M. B. , “An introduction to compressive sampling”, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.

J. Martin Duarte-Carvajalino and Sapiro, G. , “Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization”, IEEE Transactions on Image Processing, vol. 18, pp. 1395–1408, 2009.

M. Mehdi Ghahremanpour, Arab, S. Shahriar, Biookaghazadeh, S. , Zhang, J. , and van der Spoel, D. , “MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program”, Bioinformatics, vol. 30, pp. 439–441, 2013.

(192.44 KB)

C. Hegde, Sankaranarayanan, A. C. , Yin, W. , and Baraniuk, R. G. , “NuMax: A convex approach for learning near-isometric linear embeddings”, IEEE Transactions on Signal Processing, vol. 63, pp. 6109–6121, 2015.

J. Zhao, Westerham, M. , Lakatos-Toth, M. , Zhang, Z. , Moskoff, A. , and Ren, F. , “OpenICS: Open Image Compressive Sensing Toolbox and Benchmark”, Software Impacts, vol. 9, 2021.

(362.26 KB)

A. Putnam, Caulfield, A. M. , Chung, E. S. , Chiou, D. , Constantinides, K. , Demme, J. , Esmaeilzadeh, H. , Fowers, J. , Gopal, G. Prashanth, Gray, J. , and , , “A reconfigurable fabric for accelerating large-scale datacenter services”, IEEE Micro, vol. 35, pp. 10–22, 2015.

F. Ren, Park, H. , Yang, C. - K. K. , and Marković, D. , “Reference Calibration of Body-voltage Sensing Circuit for High-speed STT-RAMs”, IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 60, no. 11, pp. 2932–2939, 2013.

(1.78 MB)

Y. Wei, Zhou, J. , Wang, Y. , Liu, Y. , Liu, Q. , Luo, J. , Wang, C. , Ren, F. , and Huang, L. , “A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications”, IEEE Transactions on Biomedical Circuits and Systems , vol. 14, no. 2, pp. 145-163, 2020.

(2.08 MB)

R. Dorrance, Ren, F. , Toriyama, Y. , Hafez, A. Amin, Yang, C. - K. K. , and Marković, D. , “Scalability and Design-space Analysis of A 1T-1MTJ Memory Cell For STT-RAMs”, IEEE Transactions on Electron Devices, vol. 59, no. 4, pp. 878–887, 2012.

(1.1 MB)

F. Ren, Xu, W. , and Marković, D. , “Scalable and Parameterised VLSI Architecture for Efficient Sparse Approximation in FPGAs And SoCs”, IET Electronics Letters, vol. 49, no. 23, pp. 1440–1441, 2013.

(154.45 KB)

J. A. Tropp and Gilbert, A. C. , “Signal recovery from random measurements via orthogonal matching pursuit”, IEEE Transactions on information theory, vol. 53, pp. 4655–4666, 2007.

F. Ren, Zhang, C. , Liu, L. , Xu, W. , Owall, V. , and Marković, D. , “A Square-Root-Free Matrix Decomposition Method for Energy-Efficient Least Square Computation on Embedded Systems”, IEEE Embedded Systems Letters, vol. 6, no. 4, pp. 73–76, 2014.

(912.74 KB)

L. Cheng, Xu, W. , Ren, F. , Gong, F. , Gupta, P. , and He, L. , “Statistical Timing and Power Analysis of VLSI Considering Non-linear Dependence”, the VLSI Journal Integration, vol. 47, no. 4, pp. 487–498, 2014.

(845.26 KB)

M. Hassan Quraishi, Bank-Tavakoli, E. , and Ren, F. , “A Survey of System Architectures and Techniques for FPGA Virtualization”, IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 9, pp. 2216-2230, 2021.

(435.22 KB)

F. Ren and Marković, D. , “True Energy-performance Analysis Of The MTJ-based Logic-in-memory Architecture (1-bit Full Adder)”, IEEE Transactions on Electron Devices, vol. 57, no. 5, pp. 1023–1028, 2010.

(632.59 KB)

Miscellaneous

L. I. S. A. lab, “Deep Learning Tutorial.”. University of Montreal, 2015.

R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , “Techniques and systems for local independent failure domains”. 2017.

Patent

K. - L. Wang, Yang, C. - K. K. , Markovic, D. , and Ren, F. , “Body Voltage Sensing Based Short Pulse Reading Circuit”, PCT/US2012/056136, 2014.

F. Ren, Xu, K. , and Zhang, Z. , “LAPRAN: A SCALABLE LAPLACIAN PYRAMID RECONSTRUCTIVE ADVERSARIAL NETWORK FOR FLEXIBLE COMPRESSIVE SENSING RECONSTRUCTION”, 16/745,817, 2020.

F. Ren and Xu, K. , “Real time end-to-end learning system for a high frame rate video compressive sensing network”, US16/165,568, 2019.

D. Markovic and Ren, F. , “Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation”, US14/446,272, 2015.

Presentation