MLCAD Symposium 2024
6th ACM/IEEE International Symposium on Machine Learning for CAD
September 9-11, 2024 in Snowbird, Utah!
Starting from 2024 and after five successful events, the workshop has become the ACM/IEEE International Symposium on Machine Learning for CAD (MLCAD).
The symposium focuses on Machine Learning (ML) for all aspects of CAD and electronic system design. The symposium is sponsored by both the ACM Special Interest Group on Design Automation (SIGDA) and the IEEE Council on Electronic Design Automation (CEDA). The symposium program will have keynote and invited speakers in addition to technical presentations.
MLCAD 2024 will be held physically in Snowbird, Utah.
Advances in machine learning (ML) over the past half-dozen years have revolutionized the effectiveness of ML for a variety of applications. However, design processes present challenges that require synergetic advances in ML and CAD as compared to traditional ML applications. As such, the purpose of the symposium is to discuss, define and provide a roadmap for the special needs for ML for CAD where CAD is broadly defined to include both design-time techniques as well as run-time techniques.
Topics of interest to this symposium include but are not limited to:
• LLM-CAD: Large Language Model for CAD
• ML approaches to logic design.
• ML for physical design.
• ML for analog design.
• ML for FPGA designs.
• ML methods to predict and optimize circuit aging and reliability.
• Labeled and unlabeled data in ML for CAD.
• ML for power and thermal management.
• ML techniques for resource management in many-cores.
• ML for Design Technology Co-Optimization (DTCO).
• ML for design verification.
• ML for manufacturing test.
Sponsors for 2024 will be announced soon.
Hussam Amrouch, Technical University of Munich
Jiang Hu, Texas A&M University
Siddharth Garg, New York University
Yibo Lin, Peking University
Industry and Plenary Talk Chair
Rajeev Jain, Qualcomm
Special Session / Invited Paper Chair
Korea Advanced Institute of Science & Technology (KAIST)
Cunxi Yu, University of Maryland
Vidya A. Chhabria, Arizona State University
Hammond Pearce, University of New South Wales
Marilyn Wolf, University of Nebraska-Lincoln
Paul Franzon, North Carolina State University
Jörg Henkel, Karlsruhe Institute of Technology
Ulf Schlichtmann, Technical University of Munich
Technical Program Committee 2024
- Andreas Gerstlauer
- Anuj Pathania
- Bei Yu
- Bing Li
- Diana Goehringer
- Ioannis Savidis
- Iraklis Anagnostopoulos
- Jie Han
- Kuan-Hsun Chen
- Li Zhang
- Mehdi Saligane
- Savithri Sundareswaran
- Shao-Yun Fang
- Sneh Saurabh
- Tinghuan Chen
- Tsung-Wei Huang
- Vidya Chhabria
- Wolfgang Ecker
- Yiorgos Makris