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M. Zinkevich, Weimer, M. , Li, L. , and Smola, A. J. , Parallelized stochastic gradient descent, Advances in neural information processing systems. pp. 2595–2603, 2010.
X. Zhou, Peng, Y. , Long, C. , Ren, F. , and Shi, C. , MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time, The Thirty-seventh International Conference on Machine Learning. Virtual Event, 2020. (9.74 MB)
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)
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)
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)
X. Zhang, Xu, W. , Huang, M. - C. , Amini, N. , and Ren, F. , See UV on Your Skin: An Ultraviolet Sensing and Visualization System, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 22-28, 2013. (1.82 MB)
C. Zhang, Li, P. , Sun, G. , Guan, Y. , Xiao, B. , and Cong, J. , Optimizing fpga-based accelerator design for deep convolutional neural networks, Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, pp. 161–170, 2015.
X. Zhang, Huang, M. - C. , Ren, F. , Xu, W. , Guan, N. , and Yi, W. , Proper Running Posture Guide: A Wearable Biomechanics Capture System, Proceedings of the 8th International Conference on Body Area Networks (BodyNets). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013. (1.54 MB)
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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)
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K. Xu, Qin, M. , Sun, F. , Wang, Y. , Chen, Y. - K. , and Ren, F. , Learning in the Frequency Domain, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, pp. 1740-1749, 2020. (4.98 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)
W. Xu, Huang, M. - C. , Liu, J. J. , Ren, F. , Shen, X. , Liu, X. , and Sarrafzadeh, M. , mCOPD: Mobile Phone Based Lung Function Diagnosis and Exercise System for COPD, Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA). ACM, 2013. (646.4 KB)
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)
K. Xu, Zhang, Z. , and Ren, F. , LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction, The 15th European Conference on Computer Vision (ECCV'18). Munich, Germany, pp. 491-507, 2018. (1.41 MB)
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)
K. Xu, Learning in Compressed Domains, Arizona State University, Tempe, 2021. (18.22 MB)
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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)
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. 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. Wang, Li, X. , Yu, H. , Ni, L. , Yang, W. , Weng, C. , and Zhao, J. , Optimizing boolean embedding matrix for compressive sensing in rram crossbar, Low Power Electronics and Design (ISLPED), 2015 IEEE/ACM International Symposium on. IEEE, pp. 13–18, 2015.
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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.
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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.
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.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, vol. 61, pp. 85–117, 2015.
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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.
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)
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, Energy-performance Characterization of CMOS/Magnetic Tunnel Junction (MTJ) Hybrid Logic Circuits, University of California, Los Angeles, Los Angeles, 2011. (1.05 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)
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)
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)
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, 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)
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, Dorrace, R. , Xu, W. , and Marković, D. , A Single-precision Compressive Sensing Signal Reconstruction Engine on FPGAs, Proceedings of the 23rd International Conference on Field Programmable Logic and Applications (FPL). IEEE, pp. 1-4, 2013. (358.12 KB)
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)
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)
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.
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , Techniques and systems for local independent failure domains. 2017.
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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)
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.
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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.
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. Park, Dorrance, R. , Amin, A. , Ren, F. , Marković, D. , and Yang, C. K. Ken, Analysis of STT-RAM Cell Design With Multiple MTJs Per Access, Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH). IEEE Computer Society, pp. 53-58, 2011. (320.88 KB)
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.
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.
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J. Ouyang, Lin, S. , Qi, W. , Wang, Y. , Yu, B. , and Jiang, S. , SDA: Software-defined accelerator for large-scale DNN systems, Hot Chips 26 Symposium (HCS), 2014 IEEE. IEEE, pp. 1–23, 2014.
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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.
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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.

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