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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.
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F. Ren and Marković, D. , True Energy-performance Analysis Of The MTJ-based Logic-in-memory Architecture (1-bit Full Adder), IEEE Transactions on Electron Devices, vol. 57, no. 5, pp. 1023–1028, 2010. (632.59 KB)
R. Rangaswami, Biookaghazadeh, S. , and Lyons, S. , Techniques and systems for local independent failure domains. 2017.
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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, Under Review.
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)
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.
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)
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)
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)
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)
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.
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. 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.
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)
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)
D. Markovic and Ren, F. , Scalable and Parameterized VLSI Architecture for Compressive Sensing Sparse Approximation, US14/446,272, 2015.
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)
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)
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)
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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)
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)
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.
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.
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)
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.
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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)
Y. Bengio, Practical recommendations for gradient-based training of deep architectures, in Neural networks: Tricks of the trade, Springer, 2012, pp. 437–478.
M. Zinkevich, Weimer, M. , Li, L. , and Smola, A. J. , Parallelized stochastic gradient descent, Advances in neural information processing systems. pp. 2595–2603, 2010.
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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.
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.
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)
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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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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)
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)
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)
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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)
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.
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)
K. Xu, Learning in Compressed Domains, Arizona State University, Tempe, 2021. (18.22 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, 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)
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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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G. James, Witten, D. , Hastie, T. , and Tibshirani, R. , An introduction to statistical learning, vol. 112. Springer, 2013.
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.
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.
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.

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