Frank Schirrmeister, Sr. Group Director, Solutions & Ecosystem, Cadence Design Systems.
Electronic Design Automation (EDA) software has delivered semiconductor design productivity improvements for decades. EDA software utilizes an advanced toolkit of computational software to provide intelligent system design productivity. The next leap in productivity will come from the addition of machine learning (ML) techniques to the toolbox of computational software capabilities employed by EDA developers. Recent research and development into machine learning for EDA point to clear patterns for how it impacts EDA tools, flows, and design challenges. This presentation will update on trends and recent improvements in the verification and implementation flows for semiconductor design.
3 Key Points:
AI/ML in EDA Design Flows accelerate productivity and improve quality of results and turnaround time
Coverage closure and verification throughput can be accelerated using ML in dynamic verification
Formal verification benefits from AI/ML to optimize selection of solvers during proof orchestration
Frank is a senior group director, solutions & ecosystem at Cadence, where he leads a team translating customer challenges in the hyperscale, communications, consumer, automotive, aerospace/defense, industrial and healthcare vertical domains into specific requirements and solutions. His team focuses on cross-product technical solutions such as 5G, artificial intelligence, machine learning, safety, security and digital twins, as well as key partner collaborations. Frank holds a Dipl.-Ing. in electrical engineering from the Technical University of Berlin, Germany. Prior to joining Cadence, Frank held senior engineering and product management positions in embedded software, semiconductor and system development, both in Europe and the United States.
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