First big data and machine learning system for engineering simulation

Written by: Tom Austin-Morgan | Published:

ANSYS has released its SeaScape architecture for product developers. SeaScape is claimed to allow organisations to innovate faster than the ever by bringing together the advanced computer science of elastic computing, big data and machine learning and the physics-based world of engineering simulation.

Engineering simulation generates huge amounts of data - more than most organisations can effectively leverage for future product designs. At the same time, engineering supercomputing resources are not keeping pace with the demand for higher fidelity simulations needed for increasingly complex products. By leveraging such big data technologies as elastic compute and map reduce, SeaScape is said to provide an infrastructure to address these issues in the context of almost any engineering design objective. ANSYS says these results provide useful insights to product developers earlier in the design process so they can more quickly amend their offerings.

The first product on the SeaScape infrastructure, SeaHawk, dramatically transforms electronic product design through improvements in simulation coverage, turnaround times and analysis flexibility. The combination of big data techniques and ANSYS’ proven simulation capabilities is said to arm SeaHawk users with the capabilities to reduce size of the chip and its power consumption without sacrificing performance or schedule constraints. Early users claim to have realised an average of 5% reduction in die size, which could result in millions of dollars of savings during production.

“Die size and development time reduction are targets that electronic design engineers have pursued with marginal success given the limitations of today’s in-design solutions,” said John Lee, general manager, ANSYS. “ANSYS SeaHawk bridges the in-design and sign-off needs by bringing unprecedented simulation performance and design insights without sacrificing sign-off accuracy and coverage.”

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