IEEE | Artificial Intelligence and Machine Learning in Chip Design

In today's fast-paced technological landscape, Artificial Intelligence (AI) and Machine Learning (ML) techniques are revolutionizing chip design methodologies. Integrated-circuit (IC) chip companies and engineers have unprecedented opportunities to use these technologies to enhance product quality across crucial dimensions such as speed, energy efficiency, and cost. This, in turn, allows for meeting goals with reduced engineering resources and accelerated time-to-market.

This course program and virtual training will equip engineers with the:

Essential knowledge to leverage AI and ML effectively in chip design and electronic design automation (EDA),
Understanding of the rationale behind these technological shifts to identifying high-value applications and selecting relevant AI and ML technologies, and
Insights into optimizing design methods and preparing for the future of chip design.

Successful completion of this training will award attendees an IEEE Certificate of Completion bearing professional development hours (PDHs) and continuing education units (CEUs).

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Topics included in the course program:

Incorporating artificial intelligence and machine learning in chip design
Application types for artificial intelligence and machine learning in chip design and Electronic Design Automation
Most relevant artificial intelligence and machine learning methods for chip design and Electronic Design Automation
Infrastructure and deployment strategies for artificial intelligence and machine learning
Challenges and the future for artificial intelligence and machine learning in chip design and Electronic Design Automation

Andrew B. Kahng is an IEEE Fellow and Distinguished Professor of CSE and ECE at University of California, San Diego (UCSD). He holds an A.B. from Harvard College, and M.S. and Ph.D. from UCSD. Professor Kahng has authored three books, 500+ journal/conference papers, and 35 issued U.S. patents.

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