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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)
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)
M. Riera, Bank-Tavakoli, E. , Quraishi, M. Hassan, and Ren, F. , “HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC”. Under Review.
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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)
C. Szegedy, Liu, W. , Jia, Y. , Sermanet, P. , Reed, S. , Anguelov, D. , Erhan, D. , Vanhoucke, V. , and Rabinovich, A. , “Going deeper with convolutions”, Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9, 2015.
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E. Bank-Tavakoli, Riera, M. , Quraishi, M. Hassan, and Ren, F. , “FSCHOL: An OpenCL-based HPC Framework for Accelerating Sparse Cholesky Factorization on FPGAs”, The 33rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). Virtual Event, pp. 209-220, 2021. (380.33 KB)
T. S. Czajkowski, Aydonat, U. , Denisenko, D. , Freeman, J. , Kinsner, M. , Neto, D. , Wong, J. , Yiannacouras, P. , and Singh, D. P. , “From OpenCL to high-performance hardware on FPGAs”, Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on. IEEE, pp. 531–534, 2012.
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.
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Z. Zhang, Zhao, J. , and Ren, F. , “An Experimental Study on Transferring Data-Driven Image Compressive Sensing to Bioelectric Signals”, The 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore, pp. 1191-1195, 2022. (1.29 MB)
Z. Zhang, Yu, Z. , You, S. , Rao, R. , Agarwal, S. , and Ren, F. , “Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning”, The 48th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'23). Rhodes Island, Greece, 2023. (2.95 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)
K. Xu, Li, Y. I. , and Ren, F. , “An Energy-Efficient Compressive Sensing Framework Incorporating Online Dictionary Learning for Long-Term Wireless Health Monitoring”, Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Shanghai, China, pp. 804 - 808, 2016. (794.86 KB)
S. Biookaghazadeh, Xu, Y. , Zhou, S. , and Zhao, M. , “Enabling scientific data storage and processing on big-data systems”, Big Data (Big Data), 2015 IEEE International Conference on. IEEE, pp. 1978–1984, 2015. (966.13 KB)
B. Li and Ren, F. , “Enabling Deep Learning for Edge Computing”. 2019. (5.52 MB)
J. Friedman, Hastie, T. , and Tibshirani, R. , The elements of statistical learning, vol. 1. Springer series in statistics New York, 2001.
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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.
T. Chen, Du, Z. , Sun, N. , Wang, J. , Wu, C. , Chen, Y. , and Temam, O. , “Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning”, ACM Sigplan Notices, vol. 49. ACM, pp. 269–284, 2014.
S. Gupta, Agrawal, A. , Gopalakrishnan, K. , and Narayanan, P. , “Deep learning with limited numerical precision”, Proceedings of the 32nd International Conference on Machine Learning (ICML-15). pp. 1737–1746, 2015.
L. I. S. A. lab, “Deep Learning Tutorial.”. University of Montreal, 2015.
J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural networks, vol. 61, pp. 85–117, 2015.
A. Mousavi, Patel, A. B. , and Baraniuk, R. G. , “A deep learning approach to structured signal recovery”, Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on. IEEE, pp. 1336–1343, 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)
K. Xu, Li, Y. , and Ren, F. , “A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing”, The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New Orleans, LA, pp. 861-865, 2017. (837.1 KB)
Z. Yu, Trindade, B. Machado, Green, M. , Zhang, Z. , Sneha, P. , Bank-Tavakoli, E. , Pawlowicz, C. , and Ren, F. , “A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images”, The 29th IEEE International Conference on Image Processing (ICIP). Bordeaux, France, 2022. (1.03 MB)
Y. Chen, Luo, T. , Liu, S. , Zhang, S. , He, L. , Wang, J. , Li, L. , Chen, T. , Xu, Z. , Sun, N. , and , , “Dadiannao: A machine-learning supercomputer”, Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, pp. 609–622, 2014.
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K. Xu and Ren, F. , “CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing”, IEEE Winter Conference on Applications of Computer Vision (WACV). 2018. (821.05 KB)
Z. Zhang, Xu, K. , and Ren, F. , “CRA: A Generic Compression Ratio Adaptor for End-to-end Data-driven Image Compressive Sensing Reconstruction Frameworks”, International Conference on Acoustics, Speech, and Signal Processing. Barcelona, Spain, pp. 1740-1749, 2020. (328.74 KB)
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)
F. Ren and Marković, D. , “A Configurable 12-to-237KS/s 12.8 mW Sparse-approximation Engine for Mobile ExG Data Aggregation”, Proceedings of the 2015 IEEE International Solid-State Circuits Conference (ISSCC). IEEE, pp. 68-78, 2015. (6.94 MB)
H. Huang, Yu, H. , Zhuo, C. , and Ren, F. , “A Compressive-sensing based Testing Vehicle for 3D TSV Pre-bond and Post-bond Testing Data”, International Symposium on Physical Design (ISPD). pp. 19-25, 2016. (1.21 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.
Y. Shen, Zhu, G. , Li, J. , and Zhu, Z. , “Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning”, Proceedings of International Conference on Internet Multimedia Computing and Service. ACM, p. 193, 2014.
G. Chen and Needell, D. , “Compressed sensing and dictionary learning”, Preprint, vol. 106, 2015.
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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)
F. Ren, Park, H. , Dorrance, R. , Toriyama, Y. , Yang, C. - K. K. , and Marković, D. , “A Body-voltage-sensing-based Short Pulse Reading Circuit for Spin-torque Transfer RAMs (STT-RAMs)”, Proceedings of the 2012 13th International Symposium on Quality Electronic Design (ISQED). IEEE, pp. 275-282, 2012. (559.47 KB)
K. - L. Wang, Yang, C. - K. K. , Markovic, D. , and Ren, F. , “Body Voltage Sensing Based Short Pulse Reading Circuit”, PCT/US2012/056136, 2014.
Y. I. Li and Ren, F. , “BNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency”, International Symposium on Quality Electronic Design (ISQED). Santa Clara, CA, 2020. (186.31 KB)
M. Kim and Smaragdis, P. , “Bitwise neural networks”, arXiv preprint arXiv:1601.06071, 2016.
M. Courbariaux, Bengio, Y. , and David, J. - P. , “Binaryconnect: Training deep neural networks with binary weights during propagations”, Advances in Neural Information Processing Systems. pp. 3123–3131, 2015.
Z. Liu, Li, Y. , Ren, F. , and Yu, H. , “A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing”, The 1st International Workshop on Efficient Methods for Deep Neural Networks - Conference on Neural Information Processing Systems (NIPS). 2016. (773.3 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.
I. Hubara, Courbariaux, M. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , “Binarized neural networks”, Advances in neural information processing systems. pp. 4107–4115, 2016.

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