Dr. David Pan is a Professor, Silicon Laboratories Endowed Chair in Electrical Engineering, Fellow of IEEE and SPIE, and Director of UT Design Automation (UTDA) Lab. UTDA focuses on the research and development of design automation algorithms, methodologies, and tools for electronics, optics/photonics, and emerging technologies. The current research topics include (but are not limited to):
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement
Lin, Y., Dhar, S., Li, W., Ren, H., Khailany, B., & Pan, D. Z. (2019). DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement. The 56th Annual Design Automation Conference 2019 (DAC '19). https://doi.org/10.1145/3316781.3317803
MAGICAL: An Open- Source Fully Automated Analog IC Layout System from Netlist to GDSII
Chen, H., Liu, M., Xu, B., Zhu, K., Tang, X., Li, S., Lin, Y., Sun, N., & Pan, D. Z. (2021). MAGICAL: An Open- Source Fully Automated Analog IC Layout System from Netlist to GDSII. IEEE Design and Test, 38(2), 19–26. https://doi.org/10.1109/mdat.2020.3024153
GAN-SRAF: Subresolution Assist Feature Generation Using Generative Adversarial Networks
Alawieh, M. B., Lin, Y., Zhang, Z., Li, M., Huang, Q., & Pan, D. Z. (2021). GAN-SRAF: Subresolution Assist Feature Generation Using Generative Adversarial Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 40(2), 373–385. https://doi.org/10.1109/tcad.2020.2995338
LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks
Ye, W., Alawieh, M. B., Lin, Y., & Pan, D. Z. (2019). LithoGAN: End-to-End Lithography Modeling with Generative Adversarial Networks. The 56th Annual Design Automation Conference 2019 (DAC '19). https://doi.org/10.1145/3316781.3317852
GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance
Zhu, K., Liu, M., Lin, Y., Xu, B., Li, S., Tang, X., Sun, N., & Pan, D. Z. (2019). GeniusRoute: A New Analog Routing Paradigm Using Generative Neural Network Guidance. 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). https://doi.org/10.1109/iccad45719.2019.8942164
Dr. Andrew Kahng is a Professor in UCSD's Jacobs School of Engineering's Computer Science & Engineering and Electrical and Computer Engineering departments, where he holds the endowed chair in High-Performance Computing. He heads up the UCSD VLSI CAD Laboratory, is on the executive committee of the MARCO Design and Test focus center, and has for 12 years chaired or co-chaired the design and system drivers roadmaps in the International Technology Roadmap for Semiconductors. He is a Fellow of ACM and IEEE and was awarded the 2019 Ho-Am Prize laureate in Engineering.
Professor Kahng is an expert on the physical design of Very Large Scale Integrated circuits (VLSI), and a key strategist defining the International Technology Roadmap for Semiconductors. ITRS specifies the technology developments needed to keep pace with Moore's Law. Ever since the integrated circuit or IC was invented, transistor counts and clock speeds on microprocessors, memory, and other chips have doubled roughly every two years. Kahng is a leader in multiple efforts to maintain this pace, dubbed Moore's Law. One focus is helping to specify the next-generation computer-aided design (CAD) tools that take into account physical design aspects once left for the foundry. Problems with physical implementations of logic have been driving up costs as IC designs have grown more complex. Kahng can speak extensively about this topic and the state of the art in software for IC placement and routing, power leakage, interconnect analysis and optimization, and other physical phenomena. Kahng is a leader in "roadmapping" efforts that help rationalize research spending. Since 2000, Kahng has been chair of the Design technology working group for the International Technology Roadmap for Semiconductors. ITRS is sponsored by the major semiconductor consortia of North America, Europe, and the Far East, and also is backed by key manufacturers, suppliers, government organizations, and universities.
Machine Learning Applications in Physical Design: Recent Results and Directions
Kahng, A. B. (2018). Machine Learning Applications in Physical Design: Recent Results and Directions. Proceedings of the 2018 International Symposium on Physical Design. https://doi.org/10.1145/3177540.3177554
A new adaptive multi-start technique for combinatorial global optimizations
Boese, K. D., Kahng, A. B., & Muddu, S. (1994). A new adaptive multi-start technique for combinatorial global optimizations. Operations Research Letters, 16(2), 101–113. https://doi.org/10.1016/0167-6377(94)90065-5
Faster Minimization of Linear Wirelength for Global Placement
Alpert, C. J., Chan, T. F., Kahng, A. B., Markov, I. L., & Mulet, P. (1998). Faster Minimization of Linear Wirelength for Global Placement. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 17(1), 3–13. https://doi.org/10.1109/43.673628
New Spectral Methods for Ratio Cut Partitioning and Clustering
Hagen, L., & Kahng, A. B. (1992). New Spectral Methods for Ratio Cut Partitioning and Clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 11(9), 1074–1085. https://doi.org/10.1109/43.159993
Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning
Chan, W.-T. J., Ho, P.-H., Kahng, A. B., & Saxena, P. (2017). Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning. ISPD '17: Proceedings of the 2017 ACM on International Symposium on Physical Design. https://doi.org/10.1145/3036669.3036681
Dr. Gao was a postdoctoral researcher at the Massachusetts Institute of Technology before joining UC San Diego in 2017. He earned his Ph.D. from Carnegie Mellon in 2012. He received the NSF Career Award, Air Force Young Investigator Award, and Kurt Godel Research Fellowship Prize Silver Medal.
Dr. Gao does advanced research in automated reasoning, design automation for cyber-physical systems, and the theory of physical computing. He develops design automation techniques for cyber-physical systems, such as autonomous cars and cardiac pacemakers. He leads the development of dReal, an automated reasoning tool capable of verifying and synthesizing complex cyber-physical system designs. The tool has been used by many groups, including the Toyota Research Institute, NASA, and the Royal Victoria Infirmary in the UK.
Neural Lyapunov Control
Chang, Y., Roohi, N., & Gao, S. (2019). Neural Lyapunov Control. Conference on Neural Information Processing Systems.
Delta-Decidability over the Reals
Gao, S., Avigad, J., & Clarke, E. M. (2012). Delta-Decidability over the Reals. 2012 27th Annual IEEE Symposium on Logic in Computer Science. https://doi.org/10.1109/lics.2012.41
Delta-Complete Decision Procedures for Satisfiability over the Reals
Gao, S., Avigad, J., & Clarke, E. M. (2012). Delta-Complete Decision Procedures for Satisfiability over the Reals. Automated Reasoning, 286–300. https://doi.org/10.1007/978-3-642-31365-3_23
Delta-Decision Procedures for Exists-Forall Problems over the Reals
Kong, S., Solar-Lezama, A., & Gao, S. (2018). Delta-Decision Procedures for Exists-Forall Problems over the Reals. Computer Aided Verification, 219–235. https://doi.org/10.1007/978-3-319-96142-2_15
dReal: An SMT Solver for Nonlinear Theories over the Reals
Gao, S., Kong, S., & Clarke, E. M. (2013). dReal: An SMT Solver for Nonlinear Theories over the Reals. Automated Deduction – CADE-24, 208–214. https://doi.org/10.1007/978-3-642-38574-2_14
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