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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.
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.
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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)
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , “Techniques and systems for local independent failure domains”. 2017.
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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.
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)
M. Chen, Hu, Q. , Yu, Z. , Thomas, H. , Feng, A. , Hou, Y. , McCullough, K. , Ren, F. , and Soibelman, L. , “STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset”, The British Machine Vision Conference (BMVC). London, UK, 2022. (11.33 MB)
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)
Z. Liu, Li, Y. I. , Ren, F. , Yu, H. , and Goh, W. , “SqueezedText: A Real-time Scene Text Recognition by Binary Convolutional Encoder-decoder Network”, The AAAI Conference on Artificial Intelligence (AAAI). New Orleans, Louisana, pp. 7194-7201, 2018. (1.49 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, 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)
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)
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)
R. Dorrance, Ren, F. , and Marković, D. , “A Scalable Sparse Matrix-vector Multiplication Kernel for Energy-efficient Sparse-BLAS on FPGAs”, Proceedings of the 2014 ACM/SIGDA International Symposium on Field-programmable Gate Arrays (FPGA). ACM, pp. 161-170, 2014. (558.35 KB)
D. Markovic and Ren, F. , “Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation”, US14/446,272, 2015.
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)
R. Dorrance, Ren, F. , Toriyama, Y. , Amin, A. , Yang, C. - K. K. , and Marković, D. , “Scalability And Design-space Analysis of A 1T-1MTJ Memory Cell”, Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH). IEEE, pp. 32-36, 2011. (1.1 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)
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)
W. Li, Zhou, B. , Hsu, C. - Y. , Li, Y. I. , and Ren, F. , “Recognizing terrain features on terrestrial surface using a deep learning model - An example with crater detection”, 1st ACM SIGSPATIAL Workshop on Articial Intelligence and Deep Learning for Geographic Knowledge Discovery. ACM, Los Angeles, CA, pp. 33-36, 2017. (4.93 MB)
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.
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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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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)
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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)
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)
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Y. I. Li, Dua, A. , and Ren, F. , “Light-Weight RetinaNet for Object Detection on Edge Devices”, The 2020 IEEE World Forum on Internet of Things (WF-IoT'20). New Orleans, Louisiana, 2020. (2.43 MB)
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)
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. 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)
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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. 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.
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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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.
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)
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)
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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)
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)
B. Li and Ren, F. , “Enabling Deep Learning for Edge Computing”. 2019. (5.52 MB)
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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. 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)
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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)

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