Dr. Weidong Cao

Selected Peer-Reviewed Journal Articles

• Yuqi Liu, Weidong Cao*, Weijian Chen, Hua Wang, Lan Yang, and Xuan Zhang, “Fully integrated topological electronics,” Scientific reports 12, no. 1 (2022): 13410.

• Weidong Cao*, Changqing Wang, Weijian Chen, Song Hu, Hua Wang, Lan Yang, and Xuan Zhang, “Fully integrated parity–time-symmetric electronics,” Nature nanotechnology 17, no. 3 (2022): 262-268.

• Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han, Xuan Zhang, and Li Jiang, “Neural-PIM: Efficient processing-in-memory with neural approximation of peripherals,” IEEE Transactions on Computers 71, no. 9 (2021): 2142-2155.

• Weidong Cao, Liu Ke, Ayan Chakrabarti, and Xuan Zhang, “Evaluating neural network-inspired analog-to-digital conversion with low-precision RRAM,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 40, no. 5 (2020): 808-821.

• Weidong Cao, Xin He, Ayan Chakrabarti, and Xuan Zhang, “NeuADC: Neural network-inspired synthesizable analog-to-digital conversion,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 9 (2019): 1841-1854.

Selected Peer-Reviewed Conference Papers

• Jian Gao, Weidong Cao*, Xuan Zhang, “RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning,” In Proceedings of the 60th ACM/IEEE Design Automation Conference (DAC ’23).

• Tianrui Ma, Adith Jagadish Boloor, Xiangxing Yang, Weidong Cao, Patrick Williams, Nan Sun, Ayan Chakrabarti, Xuan Zhang, “LeCA: In-Sensor Learned Compressive Acquisition for Efficient Machine Vision on the Edge,” Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA ’23). Association for Computing Machinery, New York, NY, USA, Article 54, 1–14. https://doi.org/10.1145/3579371.3589089.

• Weidong Cao and Xuan Zhang, “A/D Alleviator: Reducing Analog-to-Digital Conversions in Compute-In-Memory with Augmented Analog Accumulation,” 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA, 2023, pp. 1-5, doi: 10.1109/ISCAS46773.2023.10181895.

• Weidong Cao, Hua Wang and Xuan Zhang, “Non-Hermitian Physics-Inspired Voltage-Controlled Oscillators with Resistive Tuning,” 2023 IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, USA, 2023, pp. 1-5, doi: 10.1109/ISCAS46773.2023.10182137.

• Dong, Zehao, Weidong Cao, Muhan Zhang, Dacheng Tao, Yixin Chen, and Xuan Zhang. “CktGNN: Circuit Graph Neural Network for Electronic Design Automation,” In The Eleventh International Conference on Learning Representations. 2022.

• Huifeng Zhu, Zhiyuan Yu, Weidong Cao, Ning Zhang, and Xuan Zhang, “PowerTouch: A Security Objective-Guided Automation Framework for Generating Wired Ghost Touch Attacks on Touchscreens,” In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD ’22). Association for Computing Machinery, New York, NY, USA, Article 67, 1–9. https://doi.org/10.1145/3508352.3549395.

• Tianrui Ma, Weidong Cao, Fei Qiao, Ayan Chakrabarti, and Xuan Zhang, “HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors,” In Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED ’22). Association for Computing Machinery, New York, NY, USA, Article 10, 1–6. https://doi.org/10.1145/3531437.3539706.

• Weidong Cao, Mouhacine Benosman, Xuan Zhang, and Rui Ma, “Domain knowledge-infused deep learning for automated analog/radio-frequency circuit parameter optimization,” In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC ’22). Association for Computing Machinery, New York, NY, USA, 1015–1020. https://doi.org/10.1145/3489517.3530501.

• Weidong Cao, Liu Ke, Ayan Chakrabarti and Xuan Zhang, “Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices,” 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Westminster, CO, USA, 2019, pp. 1-7, doi: 10.1109/ICCAD45719.2019.8942099.

• Weidong Cao, Xin He, Ayan Chakrabarti and Xuan Zhang, “NeuADC: Neural Network-Inspired RRAM-Based Synthesizable Analog-to-Digital Conversion with Reconfigurable Quantization Support,” 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy, 2019, pp. 1477-1482, doi: 10.23919/DATE.2019.8714933.

• EDAA Outstanding Ph.D. Dissertation Award Nomination, 2023
• ACM Outstanding Ph.D. Dissertation Award Nomination in Electronic Design Automation, 2023
• Best Paper Awardee at ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2022
• Best Paper Nominee at IEEE/ACM Design Automation Conference (DAC), 2022
• Outstanding Doctoral Thesis of ESE department at Washington University in St. Louis, 2022
• Best Paper Nominee of IEEE Design, Automation, & Test in Europe Conference (DATE), 2019

VLSI design, computer architecture, electronic design automation, machine learning, and quantum computing