About

The workshop focuses on Machine Learning (ML) for all aspects of CAD and electronic system design. The workshop is sponsored by both the ACM Special Interest Group on Design Automation (SIGDA) and the IEEE Council on Electronic Design Automation (CEDA). The workshop program will have keynote and invited speakers in addition to technical presentations.
MLCAD 2023 will be held physically in Snowbird, Utah.

Focus

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 workshop 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 workshop include but are not limited to:
• ML approaches to logic design.
• ML for physical design.
• ML for analog design.
• 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).

Sponsors

Diamond

Platinum

Gold

Silver

Financial Co-Sponsors

Committees

General Chairs

Andrew Kahng, University of California at San Diego
Hussam Amrouch, Technical University of Munich

Program Chairs

Jiang Hu, Texas A&M University
Bing Li, Technical University of Munich

Finance Chair

Yibo Lin, Peking University

Special Session and Panel Chair

Rajeev Jain, Qualcomm

Publication Chair

Siddharth Garg, New York University

Steering Committee

Marilyn Wolf, University of Nebraska-Lincoln
Paul Franzon, North Carolina State University
Jörg Henkel, Karlsruhe Institute of Technology
Ulf Schlichtmann, Technical University of Munich

Cover image by Jay Dash, 2016