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Because of the increasing complexity of chip designs, developing a new chip can take months or even years if firms don¡¯t forecast and install enough fixed compute capacity on-premises.

Two decades ago, a simulation could be run with only a handful of CPUs on a single server. These days, it requires partitioning a design and running multiple simulations simultaneously across dozens of CPUs. Few IT teams or data centers at chip manufacturers can keep up. 

If engineering teams don¡¯t have the customized resources they need to complete each step in the chip design flow, development will slow down, and products will miss their launch dates. As a result, computing and resource limitations have become significant bottlenecks.

Modern chip design needs a high-performance computing (HPC) environment, which requires enormous capital investment in servers, networking equipment, data-center infrastructure, utilities, and IT staff. In addition, it takes time to specify, commission, setup, and provision servers. In the meantime, engineering teams are competing for capacity and are limited in their ability to do their jobs.

Understanding Cloud-Based HPC and Compute Capacity

Cloud computing can provide a scalable, elastic HPC environment with virtually unlimited compute cycles and storage. Engineering teams get immediate access to computing, storage, and analytics resources with the cloud when needed. After peak usage, capacity is returned to the cloud service provider until the next surge in demand.

Today, HPC is used to solve complex, performance-intensive problems. Cloud-based HPC lowers the cost of product development, accelerates testing, and reduces time to market in product development and research.

In addition, HPC drives electronic design automation (EDA)-based innovation. HPC improves EDA performance in the following ways: 

• Optimized license utilization. More efficient use of EDA tools saves money and accelerates time-to-market for new chips since EDA tools are often among the most expensive line items. 
• Improved productivity. By reducing job wait times and run times, HPC enhances the productivity of existing engineers instead of hiring and training more engineers.
• Lower infrastructure cost. HPC reduces infrastructure costs, freeing up money for innovative research and development. 

Many cloud HPC services offer rapid elasticity that grows and shrinks to fit an organization¡¯s EDA workload.

Synopsys Cloud's Impact on EDA

Synopsys Cloud provides cloud-based EDA software built to leverage HPC, rapid elasticity, and other cloud characteristics to overcome fixed compute capacity that limits chip design and verification.

By reinventing and optimizing EDA workflows, Synopsys Cloud allows you to harness the full potential of cloud computing elasticity. With Synopsys products, elasticity is natively built in, making them ideal for cloud deployment.

Synopsys has partnered with industry-leading cloud providers so that you can efficiently deploy EDA in the cloud.