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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.
W. Dally, “High-performance hardware for machine learning”, NIPS Tutorial, 2015.
C. Hegde, Sankaranarayanan, A. C. , Yin, W. , and Baraniuk, R. G. , “NuMax: A convex approach for learning near-isometric linear embeddings”, IEEE Transactions on Signal Processing, vol. 63, pp. 6109–6121, 2015.
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.
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.
D. Markovic and Ren, F. , “Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation”, US14/446,272, 2015.
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)
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.
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.
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)
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)
A. Dua, “Hardware Acceleration of Video Analytics on FPGA Using OpenCL”, Arizona State University, Tempe, 2019. (2.83 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.
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)
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.
2021
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)
Y. I. Li, “Hardware-friendly Deep Learning for Edge Computing”, Arizona State University, Tempe, 2021. (7.78 MB)
K. Xu, “Learning in Compressed Domains”, Arizona State University, Tempe, 2021. (18.22 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)
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)

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