Publication

Export 106 results:
Thesis
F. Ren, “A Scalable VLSI Architecture for Real-Time and Energy-Efficient Sparse Approximation in Compressive Sensing Systems”, University of California, Los Angeles, Los Angeles, 2015. (5.71 MB)
K. Xu, “Learning in Compressed Domains”, Arizona State University, Tempe, 2021. (18.22 MB)
Y. I. Li, “Hardware-friendly Deep Learning for Edge Computing”, Arizona State University, Tempe, 2021. (7.78 MB)
A. Dua, “Hardware Acceleration of Video Analytics on FPGA Using OpenCL”, Arizona State University, Tempe, 2019. (2.83 MB)
A. Gunavelu Mohan, “Hardware Acceleration of Most Apparent Distortion Image Quality Assessment Algorithm on FPGA Using OpenCL”, Arizona State University, 2017. (1.21 MB)
F. Ren, “Energy-performance Characterization of CMOS/Magnetic Tunnel Junction (MTJ) Hybrid Logic Circuits”, University of California, Los Angeles, Los Angeles, 2011. (1.05 MB)
Report
A. Dua, Li, Y. I. , and Ren, F. , “Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing”, 2020.
Presentation
B. Li and Ren, F. , “Enabling Deep Learning for Edge Computing”. 2019. (5.52 MB)
Patent
D. Markovic and Ren, F. , “Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation”, US14/446,272, 2015.
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.
F. Ren, Xu, K. , and Zhang, Z. , “LAPRAN: A SCALABLE LAPLACIAN PYRAMID RECONSTRUCTIVE ADVERSARIAL NETWORK FOR FLEXIBLE COMPRESSIVE SENSING RECONSTRUCTION”, 16/745,817, 2020.
K. - L. Wang, Yang, C. - K. K. , Markovic, D. , and Ren, F. , “Body Voltage Sensing Based Short Pulse Reading Circuit”, PCT/US2012/056136, 2014.
Miscellaneous
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , “Techniques and systems for local independent failure domains”. 2017.
L. I. S. A. lab, “Deep Learning Tutorial.”. University of Montreal, 2015.
Journal Article
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)
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)
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)
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)
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, 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)
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)
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)
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)
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.
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)
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.
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)
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.
E. J. Candès and Wakin, M. B. , “An introduction to compressive sampling”, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
E. J. Candès and Wakin, M. B. , “An introduction to compressive sampling”, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , “Introduction to compressed sensing”, preprint, vol. 93, p. 2, 2011.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , “Introduction to compressed sensing”, preprint, vol. 93, p. 2, 2011.
W. Dally, “High-performance hardware for machine learning”, NIPS Tutorial, 2015.
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)
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.
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)
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.
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.
J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural networks, vol. 61, pp. 85–117, 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)
Y. LeCun, Bengio, Y. , and Hinton, G. , “Deep learning”, Nature, vol. 521, pp. 436–444, 2015.
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)
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.
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.
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)
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.
G. Chen and Needell, D. , “Compressed sensing and dictionary learning”, Preprint, vol. 106, 2015.
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)
M. Kim and Smaragdis, P. , “Bitwise neural networks”, arXiv preprint arXiv:1601.06071, 2016.
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.

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