The industry is catching on to the great benefits of formal verification in the cloud and adoption is steadily increasing. There are performance and time-to-results gains, and the elastic compute resources can help you to find those corner-case bugs. But did you know that it could also help improve your bottom line with improved cost of results? Check out what happened when AWS and Synopsys teamed up on scaling exercises running EDA workloads in the cloud, and how you can decrease turn-around time by up to 40x and improve convergence by up to 3x.
Getting the right EDA cloud solution is critical to meet the demands of a new era of innovation, one that includes increased computational demands and decreased verification time. In this article, you will learn about how to evaluate cloud-based EDA technologies, as we take a dive into four things you should consider: scalability with cloud-native architectures, data migration and management technologies, security and IP protection, and ease of use and deployment.
Exacting quality requirements. Demanding time-to-market requirements. Sky-high costs. Combine these pressures with the fact that Moore’s law is waning. In the face of it all, chip designers increasingly look to the cloud as the way of the future. Explore the market drivers for cloud-based chip design—faster time to results, improved quality of results, better cost of results, and high levels of system security and uptime—and you’ll want to take your next design to the cloud, too.
While time-to-market windows are shrinking, networking SoCs must handle the latest protocols and vast increases in data and traffic. Designing today is about achieving high bandwidth and low latency. Because of this, the design and verification of networking SoCs is increasingly being adopted in the cloud. Learn why cloud-based emulation is the superpower that enables acceleration of chip innovation, and discover a one-stop hosted emulation solution that is reliable, flexible, and secure.
As the lead EDA supplier in a partnership with IBM Research AI Hardware Center to improve AI performance by 1000x in ten years, we are excited to tell you about our progress. Only two years into the program, there have been several tape-outs and test chips. Most notably, a breakthrough in performance-per-watt optimization announced by IBM researchers last year. Get the update and learn about tools built for the AI era and implementing the hybrid cloud for chip design.
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