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Unexpected Help for Simulation from Machine Learning

Feb. 13, 2018 – 

I attend a lot of events on machine learning and write about it regularly. However, I learned some exciting new information about machine learning in a very surprising place recently. Every year for the last few years I have attended the HSPICE SIG dinner hosted by Synopsys in Santa Clara. This event starts with a vendor fair featuring companies that have useful interfaces and integrations with HSPICE. After a mixer in the vendor fair area, there is a dinner and several talks pertinent to the HSPICE user community.

Usually there are interesting talks by Synopsys customers on how they used the features of HSPICE in their flow to solve a vexing design problem. However, this time Synopsys threw us a curve ball. The main thrust of the talks this year focused on applications for machine learning in EDA, and specifically around HSPICE related issues. Naturally, EDA is not the first thing one thinks of when machine learning is mentioned. Yet, more and more, machine learning is being applied to difficult problems in electronics design.

It's important to point out that machine learning is as good at numerical problems as it is at visual problems. During a panel portion of the dinner event, Synopsys Scientist Dr. Mayukh Bhattacharya clearly laid out the ideal scenario for using machine learning for a numerical problem. He cited the very broad definition of machine learning as "using a set of observations to uncover an underlying process". There are three must haves: a pattern exists, we cannot pin it down mathematically, and we have data. It should be evident that these three conditions are met in a lot of SPICE related design problems.

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