Publication

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Book
J. Friedman, Hastie, T. , and Tibshirani, R. , The elements of statistical learning, vol. 1. Springer series in statistics New York, 2001.
G. James, Witten, D. , Hastie, T. , and Tibshirani, R. , An introduction to statistical learning, vol. 112. Springer, 2013.
Book Chapter
Y. Bengio, β€œPractical recommendations for gradient-based training of deep architectures”, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
Conference Proceedings
H. Park, Dorrance, R. , Amin, A. , Ren, F. , MarkoviΔ‡, D. , and Yang, C. K. Ken, β€œAnalysis 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)
S. Biookaghazadeh, Zhao, M. , and Ren, F. , β€œAre FPGAs Suitable for Edge Computing?”, The USENIX Workshop on Hot Topics in Edge Computing (HotEdge '18). BOSTON, MA, 2018. (363.22 KB)
Z. Zhang, Trindade, B. Machado, Green, M. , Yu, Z. , Pawlowicz, C. , and Ren, F. , β€œAutomatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach”, The 48th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'23). Rhodes Island, Greece, 2023. (501.02 KB)
I. Hubara, Courbariaux, M. , Soudry, D. , El-Yaniv, R. , and Bengio, Y. , β€œBinarized neural networks”, Advances in neural information processing systems. pp. 4107–4115, 2016.
Z. Liu, Li, Y. , Ren, F. , and Yu, H. , β€œA 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, Bengio, Y. , and David, J. - P. , β€œBinaryconnect: Training deep neural networks with binary weights during propagations”, Advances in Neural Information Processing Systems. pp. 3123–3131, 2015.
Y. I. Li and Ren, F. , β€œBNN Pruning: Pruning Binary Neural Network Guided by Weight Flipping Frequency”, International Symposium on Quality Electronic Design (ISQED). Santa Clara, CA, 2020. (186.31 KB)
F. Ren, Park, H. , Dorrance, R. , Toriyama, Y. , Yang, C. - K. K. , and MarkoviΔ‡, D. , β€œA 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)
Y. Shen, Zhu, G. , Li, J. , and Zhu, Z. , β€œCompressed Sensing Image Reconstruction Algorithm by Dictionary Learning”, Proceedings of International Conference on Internet Multimedia Computing and Service. ACM, p. 193, 2014.
H. Huang, Yu, H. , Zhuo, C. , and Ren, F. , β€œA 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)
F. Ren and MarkoviΔ‡, D. , β€œA 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)
Z. Zhang, Xu, K. , and Ren, F. , β€œCRA: 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)
K. Xu and Ren, F. , β€œCSVideoNet: 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)
Y. Chen, Luo, T. , Liu, S. , Zhang, S. , He, L. , Wang, J. , Li, L. , Chen, T. , Xu, Z. , Sun, N. , and , , β€œDadiannao: A machine-learning supercomputer”, Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture. IEEE Computer Society, pp. 609–622, 2014.
Z. Yu, Trindade, B. Machado, Green, M. , Zhang, Z. , Sneha, P. , Bank-Tavakoli, E. , Pawlowicz, C. , and Ren, F. , β€œA 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)
K. Xu, Li, Y. , and Ren, F. , β€œA 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)
A. Mousavi, Patel, A. B. , and Baraniuk, R. G. , β€œA deep learning approach to structured signal recovery”, Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on. IEEE, pp. 1336–1343, 2015.
S. Gupta, Agrawal, A. , Gopalakrishnan, K. , and Narayanan, P. , β€œDeep learning with limited numerical precision”, Proceedings of the 32nd International Conference on Machine Learning (ICML-15). pp. 1737–1746, 2015.
T. Chen, Du, Z. , Sun, N. , Wang, J. , Wu, C. , Chen, Y. , and Temam, O. , β€œDiannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning”, ACM Sigplan Notices, vol. 49. ACM, pp. 269–284, 2014.
S. Biookaghazadeh, Xu, Y. , Zhou, S. , and Zhao, M. , β€œEnabling 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)
K. Xu, Li, Y. I. , and Ren, F. , β€œAn 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)
Z. Zhang, Yu, Z. , You, S. , Rao, R. , Agarwal, S. , and Ren, F. , β€œEnhanced 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, Zhao, J. , and Ren, F. , β€œAn 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)
T. S. Czajkowski, Aydonat, U. , Denisenko, D. , Freeman, J. , Kinsner, M. , Neto, D. , Wong, J. , Yiannacouras, P. , and Singh, D. P. , β€œFrom OpenCL to high-performance hardware on FPGAs”, Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on. IEEE, pp. 531–534, 2012.
E. Bank-Tavakoli, Riera, M. , Quraishi, M. Hassan, and Ren, F. , β€œFSCHOL: 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)
C. Szegedy, Liu, W. , Jia, Y. , Sermanet, P. , Reed, S. , Anguelov, D. , Erhan, D. , Vanhoucke, V. , and Rabinovich, A. , β€œGoing deeper with convolutions”, Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1–9, 2015.
M. Riera, Bank-Tavakoli, E. , Quraishi, M. Hassan, and Ren, F. , β€œHALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC”. Under Review.
A. Krizhevsky, Sutskever, I. , and Hinton, G. E. , β€œImagenet classification with deep convolutional neural networks”, Advances in neural information processing systems. pp. 1097–1105, 2012.
S. Biookaghazadeh, β€œKaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems”, Networking, Architecture, and Storage (NAS), 2017 International Conference on. IEEE, pp. 1–10, 2017.
K. Xu, Zhang, Z. , and Ren, F. , β€œLAPRAN: 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, Qin, M. , Sun, F. , Wang, Y. , Chen, Y. - K. , and Ren, F. , β€œLearning in the Frequency Domain”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, pp. 1740-1749, 2020. (4.98 MB)
Y. I. Li, Dua, A. , and Ren, F. , β€œLight-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)
W. Xu, Huang, M. - C. , Liu, J. J. , Ren, F. , Shen, X. , Liu, X. , and Sarrafzadeh, M. , β€œmCOPD: 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)
X. Zhou, Peng, Y. , Long, C. , Ren, F. , and Shi, C. , β€œMoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time”, The Thirty-seventh International Conference on Machine Learning. Virtual Event, 2020. (9.74 MB)
Y. Wang, Li, X. , Yu, H. , Ni, L. , Yang, W. , Weng, C. , and Zhao, J. , β€œOptimizing 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.
C. Zhang, Li, P. , Sun, G. , Guan, Y. , Xiao, B. , and Cong, J. , β€œOptimizing 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.
M. Zinkevich, Weimer, M. , Li, L. , and Smola, A. J. , β€œParallelized stochastic gradient descent”, Advances in neural information processing systems. pp. 2595–2603, 2010.
X. Zhang, Huang, M. - C. , Ren, F. , Xu, W. , Guan, N. , and Yi, W. , β€œProper 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)
W. Li, Zhou, B. , Hsu, C. - Y. , Li, Y. I. , and Ren, F. , β€œRecognizing 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)
K. Kulkarni, Lohit, S. , Turaga, P. , Kerviche, R. , and Ashok, A. , β€œReconnet: Non-iterative reconstruction of images from compressively sensed measurements”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 449–458, 2016.
R. Dorrance, Ren, F. , Toriyama, Y. , Amin, A. , Yang, C. - K. K. , and MarkoviΔ‡, D. , β€œScalability 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)
R. Dorrance, Ren, F. , and MarkoviΔ‡, D. , β€œA 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)
J. Ouyang, Lin, S. , Qi, W. , Wang, Y. , Yu, B. , and Jiang, S. , β€œSDA: Software-defined accelerator for large-scale DNN systems”, Hot Chips 26 Symposium (HCS), 2014 IEEE. IEEE, pp. 1–23, 2014.
X. Zhang, Xu, W. , Huang, M. - C. , Amini, N. , and Ren, F. , β€œSee 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, Dorrace, R. , Xu, W. , and MarkoviΔ‡, D. , β€œA 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)
Z. Liu, Li, Y. I. , Ren, F. , Yu, H. , and Goh, W. , β€œSqueezedText: 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)
M. Chen, Hu, Q. , Yu, Z. , Thomas, H. , Feng, A. , Hou, Y. , McCullough, K. , Ren, F. , and Soibelman, L. , β€œSTPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset”, The British Machine Vision Conference (BMVC). London, UK, 2022. (11.33 MB)

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