it to the algorithm? And which algorithms are suitable for representing electronic components and systems? We have to answer all of those questions.”
CAEML's aim is to demonstrate, over a five-year period, that machine learning can be applied to modeling for many different applications within the realm of electronics design. As part of that the center will be doing foundational research on the actual machine learning on the algorithms – identifying ones that are most suitable and how to use them.
“Although we're working on many applications – signal integrity analysis, IP reuse, power delivery network design, even IC layouts and physical design – all of which require models, there are common problems that we're facing, a lot of them do pertain to working with a limited set of real measurement data,” Rosenbaum said. “Historically, machine learning theorists really only focused on the algorithm. They assumed there's an unlimited quantity of data available, and that's not realistic, at least in our domain. In order to get data you have to fabricate samples and measure them, which that's takes time and money. The amount of data, though it seems huge to us, is very small compared to what they use in the field. “
Chris Wiltz is the Managing Editor of Design News