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authorGeorge Gekov <george.gekov@arm.com>2021-11-03 14:09:43 +0000
committerGeorge Gekov <george.gekov@arm.com>2021-11-05 14:54:02 +0000
commit3f3f553f472830df57671dc3fb467da18123bb4c (patch)
treecbfb9c4b488cb3ee35155ea349e1393762890034 /docs
parent3be2623cb0ccf4a86bbf06be09d438767a960ca5 (diff)
downloadml-embedded-evaluation-kit-3f3f553f472830df57671dc3fb467da18123bb4c.tar.gz
MLECO-2526: Add instructions for Arm Virtual Hardware
Change-Id: If34a22cdc1313bd8cce4d1cd30e845eb361b93e4 Signed-off-by: George Gekov <george.gekov@arm.com>
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+- [Overview](#overview)
+ - [Getting started](#getting-started)
+
+# Overview
+
+Arm® Virtual Hardware is an accurate representation of a physical System on Chip and runs as a simple application in a Linux environment for easy
+scalability in the cloud and removes dependency from silicon availability. Powered by Amazon Web Services (AWS), developers can launch Amazon Machine Image
+(AMI) running as a virtual server in the cloud. The Arm Virtual Hardware is configured with the Corstone™-300 MPS3 based Fixed Virtual Platform (FVP),
+compiler and other tools.
+
+## Getting started
+
+To take advantage of Arm Virtual Hardware, you would need to have an AWS account and follow the steps below:
+
+ 1. Login to your AWS account.
+ 2. Go to the AWS Marketplace and search for [Arm Virtual Hardware](https://aws.amazon.com/marketplace/pp/prodview-urbpq7yo5va7g).
+ 3. Subscribe to the Arm Virtual Hardware Amazon Machine Image.
+ 4. Launch the AWS instance.
+ 5. If you want to access the Arm Virtual Hardware AWS instance via ssh, at the moment of creating the instance you would need to generate a *.pem* key.
+ You could then access the AWS instance over ssh: `$ ssh -i <mykey.pem> ubuntu@<ec2-ip-address>`.
+
+Note that you can register to receive free AWS credits to use Arm Virtual Hardware from [here](https://www.arm.com/company/contact-us/virtual-hardware).
+
+You can find more information about Arm Virtual Hardware [here](https://arm-software.github.io/VHT/main/overview/html/index.html).
+
+Once you have access to the AWS instance, we recommend starting from the [quick start guide](../quick_start.md) in order to get familiar
+with the ml-embedded-evaluation-kit. Note that on the AWS instance, the FVP is available under `/opt/FVP_Corstone_SSE-300`.