Amazon Web Services

Software Services

Services:

Cloud Connectivity, Cloud Development, Machine Vision, WebUI

Operating Systems:

Embedded Linux

AWS IoT Greengrass
AWS IoT Greengrass seamlessly extends AWS to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With AWS IoT Greengrass, connected devices can run AWS Lambda functions, execute predictions based on machine learning models, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet.

With AWS IoT Greengrass, you can use familiar languages and programming models to create and test your device software in the cloud, and then deploy it to your devices. AWS IoT Greengrass can be programmed to filter device data and only transmit necessary information back to the cloud. You can also connect to third-party applications, on-premises software, and AWS services out-of-the-box with AWS IoT Greengrass Connectors. Connectors also jumpstart device onboarding with pre-built protocol adapter integrations and allow you to streamline authentication via integration with AWS Secrets Manager.

AWS SageMaker Neo
Amazon SageMaker Neo enables developers to train machine learning models once and run them anywhere in the cloud and at the edge. It also optimizes models to run up to twice as fast, with less than a tenth of the memory footprint, with no loss in accuracy.

Developers spend a lot of time and effort to deliver accurate machine learning models that can make fast, low-latency predictions in real-time. This is particularly important for edge devices where memory and processing power tend to be highly constrained, but latency is very important. For example, sensors in autonomous vehicles typically need to process data in a thousandth of a second to be useful, so a round trip to the cloud and back isn’t possible.

There is a wide array of different hardware platforms and processor architectures for edge devices. To achieve high performance, developers need to spend weeks or months hand-tuning their model for each one. Also, the complex tuning process means that models are rarely updated after they are deployed to the edge. Developers miss out on the opportunity to retrain and improve models based on the data the edge devices collect.

AWS Association with Toradex
AWS, NXP, and Toradex have collaborated to create a computer vision reference kit that utilizes the Toradex Apalis System on Module based on the NXP i.MX8QuadMax processor, the latest and most powerful processor in NXP’s i.MX product family available in the market today. The reference kit connects a camera to the SoM and enables you to deliver AWS SageMaker Neo models which are trained in the cloud using AWS SageMaker and delivered to the edge using AWS Greengrass. The demo that is shown in the video below gives an example of a model trained to recognize various types of pasta that are glued onto a conveyor belt.

The reference kit will be available for purchase by end-2019.

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Jumpstart cloud-connected computer vision and ML designs

Cloud-enabled AI demonstration - Toradex | Amazon Web Services | NXP

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Region

Asia, Europe, North America, South America