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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. Gunavelu Mohan, “Hardware Acceleration of Most Apparent Distortion Image Quality Assessment Algorithm on FPGA Using OpenCL”, Arizona State University, 2017. (1.21 MB)
A. Dua, “Hardware Acceleration of Video Analytics on FPGA Using OpenCL”, Arizona State University, Tempe, 2019. (2.83 MB)
Y. I. Li, “Hardware-friendly Deep Learning for Edge Computing”, Arizona State University, Tempe, 2021. (7.78 MB)
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)
W. Dally, “High-performance hardware for machine learning”, NIPS Tutorial, 2015.
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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.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , “Introduction to compressed sensing”, preprint, vol. 93, p. 2, 2011.
M. A. Davenport, Duarte, M. F. , Eldar, Y. C. , and Kutyniok, G. , “Introduction to compressed sensing”, preprint, vol. 93, p. 2, 2011.
E. J. Candès and Wakin, M. B. , “An introduction to compressive sampling”, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
E. J. Candès and Wakin, M. B. , “An introduction to compressive sampling”, IEEE signal processing magazine, vol. 25, pp. 21–30, 2008.
G. James, Witten, D. , Hastie, T. , and Tibshirani, R. , An introduction to statistical learning, vol. 112. Springer, 2013.
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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.
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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)
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, “Learning in Compressed Domains”, Arizona State University, Tempe, 2021. (18.22 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)
J. Martin Duarte-Carvajalino and Sapiro, G. , “Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization”, IEEE Transactions on Image Processing, vol. 18, pp. 1395–1408, 2009.
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)
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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)
M. Mehdi Ghahremanpour, Arab, S. Shahriar, Biookaghazadeh, S. , Zhang, J. , and van der Spoel, D. , “MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program”, Bioinformatics, vol. 30, pp. 439–441, 2013. (192.44 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)
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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.
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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)
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.
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M. Zinkevich, Weimer, M. , Li, L. , and Smola, A. J. , “Parallelized stochastic gradient descent”, Advances in neural information processing systems. pp. 2595–2603, 2010.
Y. Bengio, “Practical recommendations for gradient-based training of deep architectures”, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
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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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.
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)
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.
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.
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)
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)
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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. , 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)
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)
D. Markovic and Ren, F. , “Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation”, US14/446,272, 2015.
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)
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)
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)
J. A. Tropp and Gilbert, A. C. , “Signal recovery from random measurements via orthogonal matching pursuit”, IEEE Transactions on information theory, vol. 53, pp. 4655–4666, 2007.
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)
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)
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)
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)
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)
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.

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