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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)
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)
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. 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)
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)
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. 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)
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)
Y. I. Li, Hardware-friendly Deep Learning for Edge Computing, Arizona State University, Tempe, 2021. (7.78 MB)
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)
B. Li and Ren, F. , Enabling Deep Learning for Edge Computing. 2019. (5.52 MB)
Y. LeCun, Bengio, Y. , and Hinton, G. , Deep learning, Nature, vol. 521, pp. 436–444, 2015.
L. I. S. A. lab, Deep Learning Tutorial.. University of Montreal, 2015.
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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. 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. Kim and Smaragdis, P. , Bitwise neural networks, arXiv preprint arXiv:1601.06071, 2016.
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G. James, Witten, D. , Hastie, T. , and Tibshirani, R. , An introduction to statistical learning, vol. 112. Springer, 2013.
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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.
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)
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)
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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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.
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)
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J. Friedman, Hastie, T. , and Tibshirani, R. , The elements of statistical learning, vol. 1. Springer series in statistics New York, 2001.
M. A. T. Figueiredo, Nowak, R. D. , and Wright, S. J. , Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE Journal of selected topics in signal processing, vol. 1, pp. 586–597, 2007.
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)
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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.
A. Dua, Hardware Acceleration of Video Analytics on FPGA Using OpenCL, Arizona State University, Tempe, 2019. (2.83 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.
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)
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)
D. L. Donoho and Tsaig, Y. , Fast solution of $$\backslash$ell \_ $\$1$\$ $-norm minimization problems when the solution may be sparse, IEEE Transactions on Information Theory, vol. 54, pp. 4789–4812, 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.
W. Dally, High-performance hardware for machine learning, NIPS Tutorial, 2015.
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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.
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.
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.
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)
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.
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)
G. Chen and Needell, D. , Compressed sensing and dictionary learning, Preprint, vol. 106, 2015.
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.
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.
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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)

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