Publication

Export 98 results:
To Appear
J. Zhao, Westerham, M. , Lakatos-Toth, M. , Zhang, Z. , Moskoff, A. , and Ren, F. , OpenICS: Open Image Compressive Sensing Toolbox and Benchmark, Software Impacts, To Appear. (154.37 KB)
2021
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)
2020
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)
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)
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)
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, 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)
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. 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)
2019
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)
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)
B. Li and Ren, F. , Enabling Deep Learning for Edge Computing. 2019. (5.52 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.
A. Dua, A Scalable Parameterized OpenCL-Defined Accelerator Architecture for Efficient Convolutional Neural Network (CNN) Inference on FPGAs, Arizona State University, 2019. (2.83 MB)
2018
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)
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. 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)
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)
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)
2017
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)
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)
A. Gunavelu Mohan, Hardware Acceleration of Most Apparent Distortion Image Quality Assessment Algorithm on FPGA Using OpenCL, Arizona State University, 2017. (1.21 MB)
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.
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)
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , Techniques and systems for local independent failure domains. 2017.
2016
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)
B. Hu, Ren, F. , Chen, Z. - Z. , Jiang, X. , and Chang, M. - C. Frank, 9-bit time–digital-converter-assisted compressive-sensing analogue–digital-converter with 4 GS/s equivalent speed, IET Electronics Letters, vol. 52, no. 6, pp. 430-432, 2016. (511.29 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.
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.
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. Kim and Smaragdis, P. , Bitwise neural networks, arXiv preprint arXiv:1601.06071, 2016.
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 12–237 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)
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.
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. 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.
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.
2015
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.
G. Chen and Needell, D. , Compressed sensing and dictionary learning, Preprint, vol. 106, 2015.
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)
Y. LeCun, Bengio, Y. , and Hinton, G. , Deep learning, Nature, vol. 521, pp. 436–444, 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.
J. Schmidhuber, Deep learning in neural networks: An overview, Neural networks, vol. 61, pp. 85–117, 2015.
L. I. S. A. lab, Deep Learning Tutorial.. University of Montreal, 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.
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)
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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