Ansys to Drive Major Advances in AI-Powered Semiconductor Design Using NVIDIA AI
Integration of NVIDIA Modulus AI framework with the Ansys SeaScape platform will enable engineers to easily build customized AI solutions that can improve designer productivity and quickly identify optimal design configurations
/ Key Highlights
- NVIDIA Modulus artificial intelligence (AI) framework will be integrated into the Ansys SeaScape™ cloud-optimized big data analytics platform for electronic design automation, which has demonstrated a speed-up of thermal simulation by over 100x
- The Modulus physics-informed AI techniques strongly complement
Ansys multiphysics simulation engines within SeaScape, includingAnsys power integrity and reliability signoff platforms Ansys RedHawk-SC™, Ansys Totem-SC™, Ansys PathFinder-SC™, and Ansys RedHawk-SC Electrothermal™ - The integration will improve product outcomes for applications including graphics processing units (GPUs), high-performance computing (HPC) chips, AI chips, smartphone processors, and advanced analog integrated circuits
NVIDIA Modulus is a physics-AI framework to train and deploy models that combine physics-based domain knowledge with simulation data, allowing users to create customized AI engines tailored to their needs. As AI gets integrated into computer-aided engineering workflows, it is important for users to have a seamless and integrated pipeline that allows data generated by solvers to flow to AI frameworks used to train models. Integrating NVIDIA Modulus framework into the Ansys SeaScape platform will enable customers to use high-fidelity data generated by
For example, designers can train their AI models in the integrated Modulus framework using their library of completed designs in Ansys RedHawk-SC. Once the AI is trained, it can be used to identify optimal designs based on desired specifications — such as size, power, and performance — in a fraction of the time.
"NVIDIA has been collaborating closely with
"NVIDIA Modulus makes it easy to train and deploy AI models that are physics-informed and reflect real-world causality," said
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