Home / Content / Yixing Li

Yixing Li

Fall 2015 - Spring 2021
Ph.D., Computer Engineering (CIDSE)

Email: yixingli@asu.edu

LinkedIn Profile: 
Link

Biography

Education

Ph.D. Arizona State University, 2015-present

M.S. South China University of Technology, 2012-2015

B.S. South China University of Technology, 2008-2012

Research Interests

Hardware acceleration for data analytics; compressed neural network

Dissertation/Thesis Title

Hardware-friendly Deep Learning for Edge Computing

First Job Placement After Graduation

Machine Learning Engineer, Apple

Publications

Thesis

Y. I. Li, “Hardware-friendly Deep Learning for Edge Computing”, Arizona State University, Tempe, 2021. (7.78 MB) Thesis

Journal Article

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) Journal Article
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) Journal Article
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) Journal Article

Report

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. Report

Conference Proceedings

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) Conference Proceedings
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) Conference Proceedings
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) Conference Proceedings
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) Conference Proceedings
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) Conference Proceedings
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) Conference Proceedings
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) Conference Proceedings