SANTA CLARA, Calif. — 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.
“We knew we would get no funding for one specific problem, so we decided we needed to solve them all, reaching out to other universities to work together to investigate different machine-learning techniques and algorithms that are well suited to use in electronics,” she said.
The work got backing from the National Science Foundation as well as nine companies: Analog Devices, Cadence, Cisco, Hewlett-Packard Enterprise (HPE), IBM, Nvidia, Qualcomm, Samsung, and Xilinx. The center is jointly hosted at the University of Illinois Urbana-Champaign, North Carolina State University (NCSU), and Georgia Tech.
So far, the group has identified interest areas that include high-speed interconnects, power delivery, system-level electrostatic discharge (ESD), IP core reuse, and design rule checking. Rosenbaum’s research team will explore use of recurrent neural nets to model ESD characteristics of circuits so that systems pass qualification tests the first time.
“We would like to model phenomena that we can’t using existing techniques … such as ESD characteristics that depend on a power-delivery network and multicore interactions” in a processor, she said.
One of the hurdles is finding ways to limit neural net predictions to physically valid outputs. Overall, researchers need to carefully construct each step in the machine-learning process from acquiring good training data to selecting candidate models, training them, and validating their results, she said.
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