👋 About Me

👨‍💼 I’m now working at EDA R&D Center of Hisilicon Tech. Co., Ltd. , which is one of the most competitive fabless semiconductors company all over the world (Biggest IC Design House in China), leading a lithography modeling group.

👨‍🎓 I graduated from School of Electronic Science & Engineering, Southeast University (SEU) with a bachelor’s degree (2018) and a master’s degree (2021), advised by Prof. Tang Yongming (汤勇明). I also collaborate with Wang Chengcheng (王成诚) working at T-Head , Li He (李鹤) from University of Cambridge , and Yu Feng (俞峰) from NUS closely.

🎯 My research interests include reconfigurable computing (especially using FPGA), image & video super-resolution (ISR & VSR), resolution enhancement technology (RET) and electronic design automation. Now, my current research interest is computational lithography in Hisilicon, which is also known as OPC in the field of semiconductor design and manufacturing. I have published 10+ papers at the top international conferences such as ASAP, FCCM, ICCSS.

🔥 News

2023.11: 🎖️ Our work EGVSR ⭐️800+ (Up to now)!
2023.02: 🏆 My google scholar citations have exceeded 50!
2021.09: 💼 I join Hisilicon’s EDA Team as an AI researcher in Shanghai!
2021.07: 📣 We release EGVSR on Github (Welcome to STAR and FORK)!

💬 Invited Talks and Presentations

ASAP 2021

Real-time super-resolution system of 4k-video based on deep learning

Chengcheng Wang#, He Li#, Yanpeng Cao, Changjun Song, Feng Yu, Yongming Tang

Project

Explore the possibility of real-time VSR system
Design an efficient and generic VSR network, termed EGVSR

JSEU 2022

WinoNet: Reconfigurable LUT-based Winograd Accelerator for Arbitrary Precision Convolutional Neural Network Inference

Yanpeng Cao, Chengcheng Wang, Changjun Song, Yongming Tang, He Li

Project

Investigate an area-efficient FPGA accelerator for CNN inference.
Design a Winograd-convolution hardware architecture.
Proposed for quantized neural networks with low bit-width.

📝 Publications

W. Chengcheng, L. He, C. Yanpeng, S. Changjun, Y. Feng, and T. Yongming, “WinoNet: Reconfigurable look-up table-based Winograd accelerator for arbitrary precision convolutional neural network inference,” Journal of Southeast University (English Edition) [ISSN:1003-7985/CN:32-1325/N], vol. 38, no. 2022 4, pp. 332–339, 2022.
C. YANPENG, T. YONGMING, and Z. YAOSHENG, Spherical enclosed cockpit panoramic display method based on double-projection transformation. 2022.
Y. Cao, C. Wang, C. Song, Y. Tang, and H. Li, “Real-time super-resolution system of 4k-video based on deep learning,” in 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2021, pp. 69–76.
C. Wang, Y. Cao, and Y. Tang, “P-5.5: An Adaptive Video Scaling System Implementation Based on FPGA,” in SID Symposium Digest of Technical Papers, 2021, vol. 52, pp. 531–534.
Y. Cao, C. Song, and Y. Tang, “Efficient LUT-based FPGA accelerator design for universal quantized CNN inference,” in 2021 2nd Asia Service Sciences and Software Engineering Conference, 2021, pp. 108–115.
C. Wang, Y. Cao, F. Yu, and Y. Tang, “Dynamic Weight of Adaptive Information Density Network for Image Super-Resolution,” in 2021 2nd Asia Service Sciences and Software Engineering Conference, 2021, pp. 123–129.
Y. Cao, F. Yu, and Y. Tang, “29.2: Deep Learning Based Video Super Resolution A Survey,” in SID Symposium Digest of Technical Papers, 2021, vol. 52, pp. 187–187.
Y. Cao, F. Yu, and Y. Tang, “A digital watermarking encryption technique based on FPGA cloud accelerator,” IEEE Access, vol. 8, pp. 11800–11814, 2020.
Y. Cao, C. Wang, and Y. Tang, “Explore efficient lut-based architecture for quantized convolutional neural networks on fpga,” in 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2020, pp. 232–232.
F. Yu, Y. Cao, and Y. Tang, “Realization of Quantized Neural Network for Super-resolution on PYNQ,” in 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2020, pp. 233–233.
Y. Cao and Y. Tang, “Development of Real-Time Style Transfer for Video System,” in 2019 3rd International Conference on Circuits, System and Simulation (ICCSS), 2019, pp. 183–187.
F. Yu, Y. Cao, and Y. Tang, “Portable Circuit Design Based on SERF Atomic Magnetometer,” in 2019 3rd International Conference on Circuits, System and Simulation (ICCSS), 2019, pp. 98–102.
Y. Cao, Y. Tang, and Y. Xie, “A novel augmented reality guidance system for future informatization experimental teaching,” in 2018 IEEE international conference on teaching, assessment, and learning for engineering (TALE), 2018, pp. 900–905.
S. Hu, Q. Zhu, Y. Cao, Y. Tang, and Y. Zheng, “Design of machine vision aided measurement system for near-eye display devices,” in 25th International Display Workshops, IDW 2018, 2018, vol. 2, pp. 1020–1022.

🎖 Honors and Awards

2022, Future Star, Annual award for outstanding staffs, Huawei Technologies Co., Ltd.
2020, Gong Jin Fellowship, Awarded to academic excellent students (top 1%), Education Foundation of SEU
2018, Second Prize, China Post-Graduate Mathematical Contest in Modeling, Chinese Graduate Education
2017, Xilinx Award, National College Students Smarter Innovation Competition, Xilinx, Inc. (Now AMD)
2016, Excellent League Member, Awarded to best league members in SEU, Southeast University
2016, First Prize, TI Cup Undergraduate Electronic Design Contest, NUEDC Organizer
2015, National Scholarship, Highest award for undergraduates, Chinese Ministry of Education
2015, SHK Scholarship, Sun Hung Kai & Co. Foundation, Southeast University
2014, TI Scholarship, TI China University Program, Southeast University

📖 Educations

2018.09 - 2021.06, M.S., School of Electronic Science & Engineering, Southeast Univeristy, Nanjing.
2014.09 - 2018.06, B.S., School of Electronic Science & Engineering, Southeast Univeristy, Nanjing.

💻 Internships

2019.05 - 2020.02, Hikvision, Shanghai.