Essential Cloud Computing Features for Successful Chip Design

We've all heard of the cloud, that amorphous delivery of computing services over the Internet. Myriad cloud-based solutions exist, including servers, storage, databases, networking, software, analytics, and intelligence. The article discusses the essential cloud computing features for electronic design automation (EDA) that benefit chip designers and small businesses with chip design projects.

For many chip designers, the cloud has become a path to drive innovation and productivity. With the cloud, you can scale design and verification capabilities whenever you need them. You will receive faster, higher quality results as well as better prices.

Cloud computing features offer chip design teams access to the best computing and storage resources. Chip designers benefit from quick ramp-up and flexible pay-as-you-go models.

Essential Cloud Computing Features for Chip Design

Scalability

You can scale up cloud-based systems in minutes. You can therefore quickly expand or reduce your EDA infrastructure depending on demand. Just tell the cloud service you need more capacity, and it will come online within minutes. As soon as you're done, you can turn the capacity off. To add more EDA capacity to your on-premises network, however, you would have to reassign existing capacity or add more servers, which could take months. 

Network Segmentation 

Large chip makers often have several design centers worldwide. IP for a project may need to stay within certain geographical boundaries by law or contract. Network segmentation in cloud networks can enforce rules based on geography. 

Redundancy  

Cloud redundancy and on-premises redundancy differ immensely. On-premises networks can't compete with cloud systems, which store redundant servers in both a data center and worldwide. With the cloud, your data stays safe from localized disasters. 

Single Point of Failure

EDA grids usually contain several built-in points of failure. For example, a central job dispatcher usually runs on a single node. If something disrupts that node, all EDA work halts. The same goes for EDA license, configuration management, and version-control servers. Since cloud networks are based on microservices, they don't suffer from the single-point-of-failure vulnerabilities that on-premises networks do. 

Ease of Use

Designers who are used to on-premises EDA should find the cloud much easier to use. Most notably, the cloud provides faster turnaround times due to on-demand, real-time provisioning. Implementing EDA tools in the cloud usually won’t require any special processes or knowledge on the user’s part.

Cost

The cloud allows companies to run EDA software without spending money on infrastructure for the needed processing power and runtime. Companies also won’t have to pay for ongoing maintenance and support costs. The cloud can provide transitions between low- and high-demand periods that are invisible to the end user. At the same time, though, cloud services charge for using their services, so operational costs can mount if your organization is not careful about time management. 

Time to Market

To keep up with the growth in computing requirements at advanced nodes, cloud computing allows companies to speed up the time to market. By acquiring compute resources more quickly, chip design teams can complete more daily iterations, reducing time spent on verification, reliability checking, and simulation. They also move to market faster because they are spending less time on these stages. 

Emergencies

Access to EDA technology in the cloud can help you deal with late-breaking emergencies quickly and effectively. Cloud computing allows you to resolve critical issues without causing significant disruption. 

Workflow Efficiency

Choosing cloud servers near you can reduce network latency. The performance also improves with cache-based systems. 

Innovation

To enable innovation, many companies have modernized their infrastructure in the cloud. Cloud customers can leverage advanced tools, such as automation and artificial intelligence (AI), in the chip design process. These tools can complement design expertise to boost productivity and innovation substantially.