MLCAD 2023: 2023 ACM/IEEE Workshop on Machine Learning for CAD Snowbird, UT, United States, September 11-13, 2023 |
| Conference website | https://mlcad-workshop.org |
| Submission link | https://easychair.org/conferences/?conf=mlcad2023 |
| Submission deadline | July 25, 2023 |
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.
Formal shared ACM/IEEE proceedings will be published containing all accepted papers. Accepted papers will be available in both IEEE Xplore Digital Library and ACM Digital Library.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. Papers should cover one or more aspects of applying ML to enhance CAD of electronic chips and systems. Such aspects include, but are not limited to: algorithms, tools, example applications, benchmarking, data sources and management, and connections between ML and optimization.
Submissions should be full-length papers of up to six pages (PDF format, double-column, US letter size, using the IEEE format). Submissions must be anonymous to allow a double-blind review process. Submissions exceeding 6 pages will be rejected. Submitted papers must describe original work that has not been published/accepted or is currently under review. We encourage senior researchers as well as Ph.D. students to be part of the workshop.
Paper Submission Deadline: July 25, 2023
Notification: August 5, 2023
Camera Ready Version: August 20, 2023
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Submission Web page: |
List of Topics
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).
ACM and IEEE policies
(1) By submitting your article to an ACM Publication, you are hereby acknowledging that you and your co-authors are subject to all ACM Publications Policies, including ACM's new Publications Policy on Research Involving Human Participants and Subjects. Alleged violations of this policy or any ACM Publications Policy will be investigated by ACM and may result in a full retraction of your paper, in addition to other potential penalties, as per ACM Publications Policy. (2) Please ensure that you and your co-authors obtain an ORCID ID, so you can complete the publishing process for your accepted paper. ACM has been involved in ORCID from the start and we have recently made a commitment to collect ORCID IDs from all of our published authors. The collection process has started and will roll out as a requirement throughout 2022. We are committed to improve author discoverability, ensure proper attribution and contribute to ongoing community efforts around name normalization; your ORCID ID will help in these efforts.
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
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
Venue
Snowbird, Utah
Contact
All questions about submissions should be emailed to jianghu@tamu.edu or b.li@tum.de.
Sponsors
ACM Special Interest Group on Design Automation (SIGDA) and IEEE Council on Electronic Design Automation (CEDA)