Cadence selected for machine learning contract to accelerate electronic design innovation

Cadence Design Systems was selected by the Defense Advanced Research Projects Agency (DARPA) to support the Intelligent Design of Electronic Assets (IDEA) program, one of six new programs within DARPA’s Electronics Resurgence Initiative (ERI) to use advanced machine learning techniques to develop a unified platform for a fully integrated, intelligent design flow for SoCs, systems in package and printed circuit boards. The ERI investments are the next steps in creating a more automated electronics design capability that will benefit the aerospace/defense ecosystem and the electronic industry’s commercial needs.

To fulfill the program charter over the four-year term of the contract, Cadence created the Machine learning-driven Automatic Generation of ElectronicSystems Through Intelligent Collaboration (MAGESTIC) research and development program. This program will create a foundation for system design enablement by introducing greater autonomy within the design process and developing truly design-intent-driven products. The Cadence-led team includes Carnegie Mellon University and NVIDIA, two of the most renowned machine learning leaders in the world.

The DARPA ERI programs address impending engineering and economics challenges that, if left unanswered, could challenge what has been a relentless half-century run of progress in microelectronics technology. It is now clear that the design work and fabrication required to keep pace in microelectronics is becoming increasingly difficult and expensive. The MAGESTIC program aims to address:

  • Advancing the state of the art in machine learning to develop algorithms that optimize performance
  • Extending support for advanced CMOS process nodes including 7nm and below, as well as larger process nodes
  • Automating the routing and tuning of devices to improve reliability, circuit performance, and resilience
  • Demonstrating improved power, performance and area (PPA) utilizing machine learning, analytics, and optimization
  • Staging the introductions of the technology, allowing the system to learnfrom the users and allowing users to gain an understanding of how to best leverage the tools to achieve desired results.

The program also will extend Cadence’s work in employing cloud-based design systems to handle large-scale distributed processing to speed design efforts.

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