Vendors and researchers are making significant progress applying machine learning to the thorny issues of chip design, according to a panel at DesignCon here. The use of AI in EDA was a hot topic that drew a standing-room-only crowd to the panel and spawned several papers at the event. Over the past year, the Center for Advanced Electronics through Machine Learning (CAEML) has gained four new partners. The team of 13 industry members and three universities has expanded both the breadth and depth of its work. "Last year, we focused mainly on signal integrity and power integrity, but this year, we diversified our portfolio into system analysis, chip layout, and trusted platform design -- so the diversity of the research has made the most progress," said Christopher Cheng, a distinguished technologist at Hewlett-Packard Enterprise and a member of CAEML.

Nine companies and three universities have launched a research effort to see if machine learning can solve some of the toughest problems in electronics design. The center is one of many efforts across the industry trying to tap into the emerging technology. Like many ideas in tech, "it all started in a coffee shop one afternoon," said Elyse Rosenbaum, director of the Center for Advanced Electronics through Machine Learning (CAEML). "We were facing common problems. We needed behavioral models that interfaced across electro-migration and circuit domains and didn't know how to go about getting them, given that colleagues were interested in different applications," Rosenbaum said in a panel on the topic at the DesignCon event here.