Reference architecture
The previous section provided a path to migration. This section provides an annotated reference architecture diagram that reinforces these concepts. For a deep dive into the architecture and specific AWS Services that should be used, refer to Run Semiconductor Design Workflows on AWS.
![Reference architecture diagram depicting semiconductor design on AWS.](/images/whitepapers/latest/semiconductor-design-on-aws/images/semiconductor_ra.png)
Reference architecture diagram depicting semiconductor design on AWS
Architecture diagram descriptions | |||
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1 | Determine what data is needed for proof of concept or test. | 6 | AWS compute is flexible and robust, more than capable of running semiconductor design workflows. |
2 | Transfer data into AWS via AWS Snowball, AWS Direct Connect, or using several other AWS services. | 7 | Store tools and job data on Amazon EFS, Amazon FSx for Lustre, and local disk. Optionally, move long-term data storage to Amazon S3. |
3 | Transferred data is stored in Amazon S3 buckets. You can access data stored in Amazon S3 from an Amazon EC2 instance or nearly any AWS service. | 8 | Once your data is in AWS, you can leverage other services, such as data lakes, AI/ML, and analytics. |
4 | Users access their environment through a remote desktop session or command line (ssh). | 9 | Isolating environments leads to enhanced security and limits third parties to only the data they need. |
5 | All of the infrastructure needed for semiconductor design workflows is available on AWS. | 10 | Encryption is everywhere and can be enabled with your encryption keys. |