Important Announcements

• Location: The Sunday welcome reception is in Seven Summits (Level 2). The Symposium will be in Cottonwood B&C (Level 2) and meals will be in Cottonwood A (Level 2). Read more.
• Submit your artifacts: Use the chance to receive the best artifact award and travel grants. Read more.
• Hotel and travel information: Hotel reservation is now possible. Read more.
• Student travel grants: We are pleased to offer several travel grants to students. Read more.
• Journal special issue: Following MLCAD 2024, you will be invited to submit an expanded version. Read more.
• Open peer review: MLCAD will be using OpenReview starting this year. Read more.

News

Starting from 2024 and after five successful events, the workshop has become the ACM/IEEE International Symposium on Machine Learning for CAD (MLCAD).

About

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.

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 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.

2024 Sponsors

Diamond

Platinum

Gold

Silver

Student Travel Grant Sponsored by

Financial and Technical Sponsors

Committees

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

Technical Program Committee 2024

Artifact Evaluation Committee 2024

Cover image by Jay Dash, 2016