Moving EDA Workloads to the Cloud

Are you considering moving your EDA workloads to the cloud? If so, you are not alone. Many chip makers are discovering an inviting environment in the cloud for chip design and verification. 

For example, U.K. chip maker Arm announced at the end of 2020 that it was moving most of its EDA workloads to the cloud, resulting in an expected tenfold jump in throughput for chip design and verification. Arm plans to cut its data center footprint by at least 45% and its on-premises computing infrastructure by 80% as it completes migration to the cloud.

Dutch chip maker NXP Semiconductor followed suit in 2021, announcing that it would migrate most of its EDA workloads from its data centers to the cloud. And this year, Intel Foundry Services formed an alliance with major cloud providers and EDA tool suppliers to promote secure chip design and verification in the cloud. Cloud computing dramatically improves your time to market by eliminating the uncertainty of whether on-premises storage and compute infrastructure can handle EDA workloads.

Action Plan for Moving EDA Workloads to the Cloud

So how do you move your EDA workloads to the cloud? Listed below are considerations that will encourage a smooth migration.

Determine the Best Workloads for the Cloud

EDA workloads shouldn't be moved to the cloud without a good reason. To identify the EDA workloads that will benefit most from the cloud, you should review each deployment to understand its compute and memory footprint, costs, and dependencies.

EDA workloads require specific hardware configurations. They should not be run in a standard cloud instance just because it is easy to access. In a best-case scenario, the instance may be overprovisioned. In the worst-case scenario, the workload will fail or underperform due to insufficient resources. For optimal performance, match the workload to the appropriate instance.

Weigh Data Cost

Typical EDA workloads involve gigabytes to terabytes of data. While most cloud service providers offer free data ingress, data egress usually comes at a cost. Optimal data management is crucial when determining workloads to move to the cloud and those to leave on-premises.

Focus on Adding Value

Consider both financial and functional factors when choosing a cloud architecture. Financially, infrastructure is a cost center, a tool for doing business. An enterprise must have a minimum set of capabilities in this area. As soon as you determine that cloud infrastructure would enable faster delivery of your products to the market than working on-premises, you should begin to move capacity to the cloud.

Conduct a Pilot Project

Examine the chip design process and identify a critical bottleneck that more flexible deployments could alleviate. A pilot project is an excellent way to learn about the process, discover weaknesses, and make any mistakes on a small scale. Remember, knowing what success looks like requires metrics.