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Chang Meng 孟畅Assistant Professor at Interconnected Resource-aware Intelligent Systems (IRiS) Eindhoven University of Technology (TU/e), Eindhoven, the Netherlands Email address: c.meng@tue.nl [Github] [Google scholar] [ORCID] Please feel free to contact me via email if you are interested in my research. |
I am an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). From 2023 to 2025, I was a postdoctoral researcher at the Integrated Systems Laboratory (LSI) of EPFL, where I was fortunate to work with Prof. Giovanni De Micheli. I received my Ph.D. degree in 2023 from Shanghai Jiao Tong University, supervised by Prof. Weikang Qian. I study electronic design automation (EDA). My research interests focus on logic synthesis, verification, and AI applications for emerging computing paradigms, such as approximate computing.
| C017. Jian Shi, Xuan Wang, Chang Meng, and Weikang Qian✉.
QUADOL: A Quality-Driven Approximate Logic Synthesis Method Exploiting Dual-Output LUTs for Modern FPGAs [pdf] [code] In International Workshop on Logic Synthesis (IWLS), Verona, Italy, 2025. Best Student Paper Award. |
| C016. Andrea Costamagna✉, Chang Meng, and Giovanni De Micheli.
SPFD-based delay resynthesis [pdf] In International Conference on Synthesis, Modeling, Analysis and Simulation Methods, and Applications to Circuits Design (SMACD), Istanbul, Turkey, 2025. Best Paper Award. |
| C015. Chang Meng✉, Wayne Burleson, Weikang Qian, and Giovanni De Micheli
Gradient approximation of approximate multipliers for high-accuracy deep neural network retraining [pdf] [code] In Design, Automation & Test in Europe Conference & Exhibition (DATE), Lyon, France, 2025. Acceptance rate: 25%. |
| C014. Chang Meng✉, Mingfei Yu, Hanyu Wang, Wayne Burleson, and Giovanni De Micheli
RareLS: Rarity-reducing logic synthesis for mitigating hardware trojan threats [pdf] [code] In International Conference on Computer-Aided Design (ICCAD), New Jersey, USA, 2024. Acceptance rate: 24.0%. |
| C013. Toshiaki Koike-Akino✉, Chang Meng, Volkan Cevher and Giovanni De Micheli
Hardware-efficient quantization for green custom foundation models In International Conference on Machine Learning (ICML) Workshop, Vienna, Austria, 2024. |
| C012. Hanyu Wang, Chang Meng✉, and Giovanni De Micheli
Global crossover: An evolution strategy for logic synthesis In International Workshop on Logic and Synthesis (IWLS), Zurich, Switzerland, 2024. |
| C011. Chang Meng, Hanyu Wang, Yuqi Mai, Weikang Qian✉, and Giovanni De Micheli✉
VECSEM: Verifying average errors in approximate circuits using simulation-enhanced model counting [pdf] [code] Design, Automation & Test in Europe Conference & Exhibition (DATE), Valencia, Spain, 2024. Best Paper Nomination. Acceptance rate: 25%. |
| C010. Xuan Wang, Zheyu Yan, Chang Meng, Yiyu Shi, and Weikang Qian✉
DASALS: Differentiable architecture search-driven approximate logic synthesis [pdf] International Conference on Computer Aided Design (ICCAD), San Francisco, USA, 2023. Acceptance rate: 22.9%. |
| C009. Chang Meng, Jiajun Sun, Yuqi Mai, and Weikang Qian✉
MECALS: A maximum error checking technique for approximate logic synthesis [pdf] [code] Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 2023. Acceptance rate: 25%. |
| C008. Xingyue Qian, Chang Meng, Xiaolong Shen, Junfeng Zhao, Leibin Ni, and Weikang Qian✉
High-accuracy low-power reconfigurable architectures for decomposition-based approximate lookup table [pdf] Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 2023. |
| C007. Chang Meng$, Xuan Wang$, Jiajun Sun, Sijun Tao, Wei Wu, Zhizhang Wu, Leibin Ni, Xiaolong Shen, Junfeng Zhao, and Weikang Qian✉ ($equal contribution)
SEALS: Sensitivity-driven efficient approximate logic synthesis [pdf] Design Automation Conference (DAC), San Francisco, CA, USA, 2022. Acceptance rate: 23%. |
| C006. Chang Meng, Zhiyuan Xiang, Niyiqiu Liu, Yixuan Hu, Jiahao Song, Runsheng Wang, Ru Huang, & Weikang Qian✉
DALTA: A decomposition-based approximate lookup table architecture [pdf] International Conference on Computer-Aided Design (ICCAD), Munich, Germany, 2021. Acceptance rate: 23.5%. |
| C005. Yifan Qian, Chang Meng, Yawen Zhang, Weikang Qian✉, Runsheng Wang, and Ru Huang
Approximate logic synthesis in the loop for designing low-power neural network accelerator [pdf] International Symposium on Circuits and Systems (ISCAS), Daegu, South Korea, 2021. |
| C004. Zuodong Zhang, Runsheng Wang, Zhe Zhang, Ru Huang, Chang Meng, Weikang Qian, and Zhuangzhuang Zhou,
Reliability-enhanced circuit design flow based on approximate logic synthesis [pdf] Great Lakes Symposium on VLSI (GLSVLSI), Beijing, China, 2020. |
| C003. Chang Meng, Weikang Qian✉, and Alan Mishchenko
ALSRAC: Approximate logic synthesis by resubstitution with approximate care set [pdf] [code] Design Automation Conference (DAC), San Francisco, CA, USA, 2020. Acceptance rate: 23.2%. |
| C002. Chang Meng, Paul Weng, Sanbao Su, and Weikang Qian✉
Advanced ordering search for multi-level approximate logic synthesis [pdf] International Workshop on Logic and Synthesis (IWLS), Lausanne, Switzerland, 2019. |
| C001. Zhuangzhuang Zhou, Yue Yao, Shuyang Huang, Sanbao Su, Chang Meng, and Weikang Qian✉
DALS: Delay-driven approximate logic synthesis [pdf] International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA, 2018. |
| J003. Chang Meng, Alan Mishchenko, Weikang Qian✉, and Giovanni De Micheli
Efficient resubstitution-based approximate logic synthesis [pdf] [code] In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025. |
| J002. Chang Meng, Zhuangzhuang Zhou, Yue Yao, Shuyang Huang, Yuhang Chen, and Weikang Qian✉
HEDALS: Highly efficient delay-driven approximate logic synthesis [pdf] [code] IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023. |
| J001. Sanbao Su$, Chang Meng$, Fan Yang, Xiaolong Shen, Leibin Ni, Wei Wu, Zhihang Wu, Junfeng Zhao, and Weikang Qian✉ ($equal contribution)
VECBEE: A versatile efficiency-accuracy configurable batch error estimation method for greedy approximate logic synthesis [pdf] [code] IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2022. |