Schedule

Session 1  

Chairs: Sachin S. Sapatnekar (UMN) and Mike Quinn (TAMU)

10:00 – 10:10 a.m.

Welcome Message, Dean Andrew Alleyne, College of Science and Engineering, UMN

10:10 – 10:15 a.m.

Opening, Sankar Basu (NSF)

10:15 – 10:35 a.m.

Bars and Barriers to Overcome for Shared ML EDA Instructure, Andrew B. Kahng (UCSD) [slides]

10:35 – 10:55 a.m.

Engineering the Flywheel of AI for Electronic Design Automation: Present Challenges and Future Opportunities, Ruchir Puri (IBM)

10:55 – 11:15 a.m.  

AI for Chip Design - An Industry Perspective, Thomas Andersen (Synopsys)

11:15 – 11:35 a.m. 

ML for Data-Driven Verification, Dan Yu (Siemens EDA) [slides]

11:35 – 11:55 a.m.  

Towards Large, High Quality and Open Datasets for ML4EDA, Siddharth Garg (NYU) [slides]

Session 2

Chair: Jiang Hu (TAMU)

12:30 – 12:50 p.m.  

Generating ML Datasets for Digital and Analog EDA: Opportunities and Challenges, Sachin S. Sapatnekar (UMN) [slides]

12:50 – 1:10 p.m. 

Enabling Generative AI and GPU Acceleration for EDA, Mark Ren (Nvidia) [slides]

1:10 – 1:30 p.m.  

Practical Considerations for Scaling AI/ML in an EDA Context, Scot Weber (AMD)

1:30 – 2:30 p.m. Breakout Session

1. Data: raw data or scripts? format, scope & pitfalls (S. Garg, T.-W. Huang)

2. Software interface between ML/EDA tools: scope &  pitfalls (V. Chhabria, M. Robbins)

3. Open-source environment and platform extensibility (T. Ansell, C. Yu)

4. Testcases, benchmark and validation systems (M. Quinn, I. Bustany)

5. Collaboration between industry and academia (Y. Chen, C. Alpert)

6. Analog design automation (D. Pan, J. Hu)

Session 3

Chair: Yiran Chen (Duke)

2:45 – 3:00 p.m. 

Eric Schmidt, Michelle Ritter (Steel Perlot)

3:00 – 4:00 p.m. 

Summary of breakout discussion

4:00 – 5:00 p.m. 

Panel: Towards Pervasive AI in EDA through a Shared ML Infrastructure

Moderator: Ismail Bustany (AMD)

Panelists: Srinivas Bodapati (Intel), Joe Jiang (Google), Sung-Kyu Lim (DARPA), Marcus Pan (SRC), Matt Robbins (Steel Perlot)