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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 25, 2024(AoE)
Paper Submission: June 01, 2024(AoE)
Notification: July 13, 2024
Symposium: September 9-11, 2024

Call for Papers

View the call for papers and register
your paper until May 25, 2024(AoE)
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

Finance Chair
Cunxi Yu, University of Maryland

Special Session / Invited Paper Chair
Youngsoo Shin,
Korea Advanced Institute of Science & Technology (KAIST)

Publication Chair
Hammond Pearce, University of New South Wales

Publicity Chair
Vidya A. Chhabria, Arizona State 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

Technical Program Committee 2024

  • Andreas Gerstlauer
  • Anna Goldie
  • 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