Virtual Workshop: AI for CAD/EDA – Challenges and Opportunities

December 8, 2020, 1:00 pm to 5:00 pm EST
Sponsored by Huawei

Electronic Design Automation (EDA) of advanced CMOS circuits, heterogeneous and domain-specific computing architectures, silicon photonics, etc., leverage advancements in computing and algorithmic methods to reduce development cycles and improve the quality of results. Researchers in Canada have made strong contributions to EDA, leading to new products and commercial startups. Canada’s complementary focus on AI creates a unique opportunity and competitive advantage to drive next-generation EDA/CAD featuring novel AI techniques to address microsystems design challenges.

The goal of this ½-day workshop is to bring AI and EDA/CAD researchers in Canada together to present leading-edge work and discuss opportunities for collaboration and related infrastructure requirements to enable next-generation EDA/CAD tools powered by AI.

Types of questions for discussion:

  • Which areas of EDA/CAD have the best potential to utilize AI/ML techniques?
  • Are the AI/ML approaches scalable? Can they be generalized to different designs? Can they achieve better results than traditional EDA solutions?
  • What infrastructure is needed? What training datasets are required and how can these be made available?

Target Attendees

The prime target attendees are researchers in AI/machine learning, CAD/EDA, or both, who wish to:

  • Share insights and experiences with others; explore collaboration opportunities and connect with leaders from academia and industry
  • Promote innovation and adoption of advanced technologies
  • Influence CMC’s technology selection (roadmap) and development activities for emerging AI trends.


You have a computer running Zoom and an internet connection.

Preliminary Agenda

TimePresenterOrganizationPresentation Title
1:00 pmHugh Pollitt-SmithCMC MicrosystemsWelcome
1:10 pmDr. André Ivanov and Sebastian ZhouUniversity of British Columbia

Neural Network for EDA Routability-driven Global Routing

View Presentation

1:30 PMDr. Brett MeyerMcGill University

Probabilistic Sequential Multi-Objective Optimization of Convolutional Neural Networks

View Presentation

1:50 pmDr. Laleh BehjatUniversity of Calgary

When will machines make machines? Using machine learning to develop EDA tools through a physical design perspective.

View Presentation

2:10 pmDr. Nachiket KapreUniversity of WaterlooFPGA CAD and Design Optimization with Machine Learning
2:30 pmBreak  
2:50 pmDr. Matthew E. TaylorUniversity of Alberta & Alberta Machine Intelligence Institute (Amii)

Reinforcement Learning for Compilers and Chip Design

View Presentation

3:10 pmDr. Shawki AreibiUniversity of Guelph

Integrating Machine Learning Within FPGA Placement

View Presentation

3:30 pmDr. Yuri GrinbergNational Research Council Canada

A role of dimensionality reduction in high dimensional design processes: case study in silicon photonics

View Presentation

3:50 pmDr. Huang YuHuaweiDeep Learning in EDA
4:10 pmPanel and Open Discussion
5:00 pmWorkshop close

Pricing and Registration

Attendee GroupPriceRegistration
General Registrant$0*         Registration closed.

*This event is sponsored by Huawei and therefore is at no cost to the participants.


If you have any comments or questions regarding the course content or registration, please contact Hugh Pollitt-Smith (email: pollitt-smith@cmc.ca).


Course cancellations must be received in writing at least one (1) week before the beginning date of the course in question to receive a full refund of the registration fee. A cancellation made after the deadline will not receive a refund. CMC Microsystems makes no commitments on refunds for travel or accommodations.

Planning an event, course or departmental meeting? CMC is interested in supporting and participating in your events.  

Scroll to Top

CMC Planned Service Disruption

Thursday, February 4
7 am to 9 am EST

CMC is performing upgrades on our datacenter infrastructure that will temporarily affect access to CMC online services. We apologize for the inconvenience this will cause.

We apologize for the inconvenience this may cause.