MLCAD Symposium 2024
6th ACM/IEEE International Symposium on Machine Learning for CAD
September 9-11, 2024 in Snowbird, Utah!
Important Dates
Abstract Submission: May 18, 2024
Full Paper Submission: May 25, 2024
Notification: July 6, 2024
Symposium: September 9-11, 2024
Call for Papers
View the call for papers and submit your work until May 18, 2024.
Submission website is now open!
Location
Snowbird
9385 S. Snowbird Center Dr.
Snowbird, UT 84092-9000.
Important Announcements
- 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.
2023 Sponsors
Sponsors for 2024 will be announced soon.
Diamond
Platinum
Gold
Silver
Committees
General Chairs
Hussam Amrouch, Technical University of Munich
Jiang Hu, Texas A&M University
Program Chairs
Siddharth Garg, New York University
Yibo Lin, Peking University
Industry and Plenary Talk Chair
Rajeev Jain, Qualcomm
Special Session / Invited Paper Chair
Youngsoo Shin,
Korea Advanced Institute of Science & Technology (KAIST)
Finance Chair
Cunxi Yu, University of Maryland
Publicity Chair
Vidya A. Chhabria, Arizona State University
Publication Chair
Hammond Pearce, University of New South Wales
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
- Andreas Gerstlauer
- Anthony Agnesina
- Anuj Pathania
- Bei Yu
- Bing Li
- Diana Goehringer
- Guilherme Paim
- Guojie Luo
- Hongce Zhang
- Ioannis Savidis
- Iraklis Anagnostopoulos
- Jaeyong Chung
- Jie Han
- Johann Knechtel
- Kuan-Hsun Chen
- Li Zhang
- Mehdi Saligane
- Mohamed Baker Alawieh
- Nan Wu
- Nima Karimpour Darav
- Paul R. Genssler
- Qi Sun
- Savithri Sundareswaran
- Seokhyeong Kang
- Shao-Yun Fang
- Sneh Saurabh
- Subhendu Roy
- Takashi Sato
- Tinghuan Chen
- Tsung-Wei Huang
- Victor van Santen
- Vidya Chhabria
- Wolfgang Ecker
- Yiorgos Makris
- Youngsoo Shin
- Zhiyao Xie
Cover image by Jay Dash, 2016