Arm Accelerates Speed to Market by Migrating EDA Workflows to AWS Batch

Arm Limited (Arm) is a global leader in the development of licensable compute technology for semiconductor companies. As of February 2022, over 200 billion chips have been shipped that are based on Arm’s architecture and manufactured by its partners over the last 3 decades. However, the company’s on-premises data centers could not grow with the pace of engineering requirements, and in 2016, Arm decided it needed to make significant changes to achieve its projected growth target for the next 5–10 years. By migrating from on-premises data centers to Amazon Web Services (AWS), Arm created a scalable and reliable cloud-based solution for running EDA workloads. Using this solution, the company has optimized its compute costs, increased its engineering productivity, accelerated speed to market for its products, and enhanced its product quality. Additionally, using CPUs on AWS that are based on Arm architecture for the design and verification of new Arm chips has helped it to drive business success.

“Using AWS Batch facilitates selecting different instance types and mixing them together. That helps us to achieve the scalability that we need.” Zhifeng Yun, Technical Director, Arm Limited

Modernizing Its Solution to Accommodate Future Growth

Arm, a semiconductor and software design company based in the United Kingdom, wanted to modernize its engineering solution. The company’s on-premises data centers didn’t position Arm for future growth. “We couldn’t do any of the customization or optimization that we needed to do,” says Zhifeng Yun, technical director at Arm. “We didn’t have a sustainable plan to drive efficiency or to reduce the total cost of ownership given the growing engineering requirements.” The company also wanted to advance its business intelligence and create a delivery engineering road map. In 2016, Arm evaluated different cloud providers and ultimately decided to use AWS. “We chose AWS because it has highly sophisticated infrastructure and services,” says Yun. “It offers a lot in terms of the variety of instance types as well as the customer focus and support we need to get things moving more quickly.”

Arm evaluated its internal workloads, weighing the technical difficulty of migrating each one against the benefits it would bring to the business. “Our number one concern is about the quality of the product, and number two is about the time to market,” says Yun. “If we delay bringing our product to market, the impact to the entire industry could be huge. And that means a big cost not only in terms of revenue but also in terms of Arm’s reputation.” After its evaluation was complete, Arm decided to prioritize its most compute-heavy verification workloads for the migration. These workloads involve running millions of jobs—such as those that help verify the design of the CPU core—in parallel. Rather than using a lift-and-shift approach to the migration, Arm opted to modernize immediately to take advantage of cloud-native technology and managed services.