
NXP Semiconductors N.V. has announced that it is moving the vast majority of its electronic design automation workloads to Amazon Web Services Inc.’s public cloud platform.
The company has selected AWS as its preferred cloud provider. By moving its EDA workloads to Amazon’s cloud, NXP will gain increased efficiency and more compute power that should help it to design a new generation of faster and more powerful computer chips for the automotive, industrial “internet of things,” mobile and communications infrastructure sectors.
According to Amazon, NXP has already seen benefits that include enhanced collaboration and increased EDA throughput since moving to AWS, while reducing costs and gaining more time to focus on actual design, rather than managing compute resources.
Each new chip design is put through extensive testing and validation before it’s manufactured to ensure it is functionally safe and secure and delivers the expected performance. This work includes front-end design workflows such as performance simulation and verification, as well as back-end workloads around timing and power analysis, design rule checks and other applications necessary to prepare a new chip for production.
Shifting to the cloud also enables NXP to leverage key AWS analytics and machine learning services that can aid its research and development efforts. For instance, NXP is already using Amazon QuickSight, a machine learning-powered business intelligence service, to boost workflow efficiencies. By rapidly translating the results from one step of testing into modifications for another, it can reduce the time it takes to iterate on chip designs, NXP said.
The company also makes use of Amazon SageMaker, a service that’s used to build, train and deploy machine learning models in the cloud and at the edge, to optimize the way it structures compute, storage and third-party software application licenses.
NXP also benefits from the wide range of specialized instances available on AWS that allow it to achieve the perfect balance of price/performance for its EDA workflows.
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