Publication

Export 101 results:
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, Practical 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. , 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)
S. Biookaghazadeh, Zhao, M. , and Ren, F. , Are FPGAs Suitable for Edge Computing?, The USENIX Workshop on Hot Topics in Edge Computing (HotEdge '18). BOSTON, MA, 2018. (363.22 KB)
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.
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, 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.
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)
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)
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.
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)
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)
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)
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)
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.
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)
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.
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.
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. 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)
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)
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.
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 International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). Virtual Event, 2021.
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.
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.
A. Krizhevsky, Sutskever, I. , and Hinton, G. E. , Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems. pp. 1097–1105, 2012.
S. Biookaghazadeh, Kaleido: 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. , 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, 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)
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)
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)
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)
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.
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.
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. 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)
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)
K. Kulkarni, Lohit, S. , Turaga, P. , Kerviche, R. , and Ashok, A. , Reconnet: 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 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)
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)
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.
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, 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)
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)
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. 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.
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.
Journal Article
Y. I. Li, Liu, Z. , Liu, W. , Jiang, Y. , Wang, Y. , Goh, W. Ling, Yu, H. , and Ren, F. , A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing, IEEE Transactions on Industrial Electronics (TIE), vol. 66, no. 9, pp. 7407-7416, 2019. (3.34 MB)
B. Hu, Ren, F. , Chen, Z. - Z. , Jiang, X. , and Chang, M. - C. Frank, An 8-Bit Compressive Sensing ADC With 4GS/s Equivalent Speed Utilizing Self-Timed Pipeline SAR-Binary-Search, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 63, no. 10, pp. 934-938, 2016. (1.8 MB)

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