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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)
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)
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)
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, 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 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)
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)
B. Li and Ren, F. , Enabling Deep Learning for Edge Computing. 2019. (5.52 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, Hardware-friendly Deep Learning for Edge Computing, Arizona State University, Tempe, 2021. (7.78 MB)