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Amazon and Synopsys ink multi-year billion-dollar deal in multi-year IP agreement to accelerate AI chip design efforts — Synopsys to adopt Amazon Bedrock to deploy AI agents, harnessing AWS compute and storage capabilities Amazon and Synopsys team up. Amazon and chip design tool maker Synopsys are entering a multi-year deal, said to be worth over a billion dollars, in which the two companies will deepen their cooperation on AI chip design, while better optimizing their tools for one another's services. As part of the deal, Amazon will license Synopsys' IP and expand its use of Synopsys' electronic design automation (EDA) software tools for AI chip design and agentic AI technologies. The deal goes both ways. From its side of the equation, Synopsys will work with Amazon to optimize its multiphysics solutions for Amazon Trainium and Graviton chips, will adopt AWS cloud computing and storage services, and will begin using Amazon Bedrock to build and deploy AI applications and agents for its own development work. Although there's clearly some element of cooperative back-scratching with this deal, Synopsys and its competitors are moving towards automating ever greater portions of the chip design process. Amazon ensuring that process is optimized for Amazon hardware places it in a much more favorable position in a world where chip design is easier and faster. Especially with many of the major AI companies looking to develop their own inferencing hardware to ease costs from pricey Nvidia GPUs. Building the future, together Many of the major AI developments in 2026 have centered around the use of AI agents, leading to hardware shortages and a race to fill that gap with optimized hardware. The Synopsys/Amazon deal could put both companies in a position to take advantage of that, designing and developing new AI hardware, while better integration could benefit customers using both companies' services and components. The core of the deal, however, is in chip design collaboration. Amazon will expand its use of Synopsys' designs to include blueprints of application-optimized IP: silicon designs that it can incorporate into its own chips. It will also take advantage of Synopsys' AI-powered engineering software to accelerate its development of custom AI chips and AWS infrastructure hardware. Considering Amazon already markets its Graviton 5 for CPU-intensive agentic AI workloads, accelerating the development of next-generation designs could help further cement Amazon's position as it looks to compete with Nvidia on AI data center deployment. In the announcement, Amazon also cites its Trainium chips for AI training, and Nitro for cloud security, networking, and storage, suggesting the collaboration between the two companies could augment multiple chip lines. Taking a step back from the chip design process, this deal will also see Amazon and Synopsys collaborate on better applying AI within their own workflows, optimizing the silicon-to-system process to make chip design faster and more efficient. Amazon will deploy Synopsys' AI-powered EDA, physics-based simulations, and agentic AI solutions to improve the capabilities of its engineering teams. The companies claim this will help them design, analyze, optimize, and validate new chip designs more efficiently. "As our chip designs grow more ambitious and AI reshapes the engineering process itself, Synopsys helps us move faster across the design cycle, helping us deliver more capable, efficient computing for customers worldwide," said Amazon SVP Peter DeSantis in a joint press release. With Synopsys and its competitors Cadence and Siemens all pushing for faster, more autonomous chip design, we may see a shortening of the typical design cycle for new enterprise hardware. If that proves true, keeping up with that new pace will be paramount for companies like Amazon and Synopsys. It goes both ways Alongside Amazon's expanding use of Synopsys technologies, Synopsys itself will adopt AWS compute and storage services to accelerate its own IP and software development efforts. It will also use Amazon Bedrock to build and deploy AI applications and agents to develop its own product offerings. This embeds Amazon cloud services and AI tools further within Synopsys' engineering workflows, while Synopsys' intellectual property becomes more deeply integrated in Amazon's custom silicon. With their joint plan to accelerate Synopsys multiphysics solutions on Amazon's Trainium and Graviton, Amazon's hardware could be more attractive to customers running those engineering workloads. Growing that relationship holds further financial incentives for Synopsys, too. The IP agreement introduces a license-plus-royalty model, so as production volume increases, Synopsys royalty revenue could scale with it. With Amazon as its lead customer for the application-optimized IP, this deal could act as a strong endorsement for its chip design blueprints, making it easier to pitch its AI-powered tools, integrated with its IP, to other chip developers. For Amazon, this partnership should go beyond accelerating its own custom chip designs. It could strengthen the case for AWS services, with optimization of Synopsys tools and services a useful benefit, as well as both companies benefiting from jointly optimizing the chip design process with agentic AI augmentation. Faster chip design doesn't necessarily mean better chips or a shorter time to market, but if the collaboration with Synopsys helps Amazon improve the performance or efficiency of its design, even modest gains could make its AWS infrastructure more attractive and competitive. But with no announcements or suggested timeline for new chip development as of yet, both firms will need to demonstrate the effectiveness of this partnership before anyone can measure how accurate that billion-dollar estimation truly is. Jon Martindale is a contributing writer for Tom's Hardware. For the past 20 years, he's been writing about PC components, emerging technologies, and the latest software advances. His deep and broad journalistic experience gives him unique insights into the most exciting technology trends of today and tomorrow.
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