Publication

Export 103 results:
Presentation
B. Li and Ren, F. , Enabling Deep Learning for Edge Computing. 2019. (5.52 MB)
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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