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Enhance Your EDA Tools with Machine Learning
Over the years, the EDA industry has offered many solutions in the modeling and design creation of complex systems. Most design problems in EDA are NP-hard; there are simply no polynomial-time algorithms to solve these problems and hence an optimal solution cannot be identified analytically. Furthermore, the interdependence of design requirements exacerbates these problems, requiring concurrent optimization across multiple design cycle stages.
Self-Optimizing Design Tools
New, ML-driven capabilities can be embedded in different design engines, giving EDA developers a new arsenal of solutions for today’s demanding semiconductor design environment. With an abundance of data and a rich set of heuristics, new classes of ML models can be created using ensemble methods (e.g., linear regression, support vector machines, neural networks) to exploit opportunities throughout the design cycle.
International Symposium on Physical Design - Best Paper Award 2021
Synopsys wins for Machine Learning-Enabled High-Frequency Low-Power Digital Design Implementation at Advanced Process Nodes by Siddhartha Nath and Vishal Khandelwal, Synopsys
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How ML can self-optimize tools
Tools that look for DRC hotspots, EM/IR distribution, and more
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