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# Semiconductor and Electronics on AWS - High Level
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 The following architecture diagram shows migration of workflows to AWS and a high-level architecture for running semiconductor design workflows. In this architecture diagram, the infrastructure that is running on AWS is similar to the previous on-premises environment diagram. This simple architecture helps you understand the high-level approach without the need for knowing the details about each of the services that is used. 

![This annotated diagram shows a high level architecture of implementing a semiconductor environment on the AWS Cloud.](http://docs.aws.amazon.com/whitepapers/latest/run-semiconductor-workflows-on-aws/images/semiconductor-design-high-level.png)


**Semiconductor and Electronics Design on AWS - High Level** 

 **Table 1 – Semiconductor and Electronics Design on AWS - High Level desciptions** 


| Callout |  Description | 
| --- | --- | 
|  1  |  Determine what data is needed for proof of concept or test.  | 
|  2  |  Transfer data into AWS via AWS Snowball Edge, AWS Direct Connect, or using several other AWS services.  | 
|  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.  | 
|  4  |  Users access their environment through a remote desktop session or command line (ssh).  | 
|  5  |  All of the infrastructure needed for semiconductor design workflows is available on AWS.  | 
|  6  | AWS compute is flexible and robust, more than capable of running semiconductor design workflows  | 
|  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.  | 
|  8  | Once your data is in AWS, you can leverage other services, such as data lakes, AI/ML, and analytics.  | 
|  9  | Isolating environments leads to enhanced security and limits third parties to only the data they need.  | 
|  10  | Encryption is everywhere and can be enabled with your encryption keys.  |