Accelerating Distributed AI Applications
Let’s take a quick look at some of the key highlights and insights from a recent webinar, Accelerating Distributed AI Applications, with Ziad Asghar, Vice President, Product Management, Qualcomm Technologies, Inc. and resources that developers can use to build distributed AI solutions today.
In a recent webinar, Accelerating Distributed AI Applications, Ziad Asghar, Vice President, Product Management, Qualcomm Technologies, Inc., gave an insightful and pragmatic overview where distributed AI is today and the Snapdragon® mobile platforms behind it.
Let’s take a quick look at some of the key highlights and insights from the webinar and resources that developers can use to build distributed AI solutions today.
5G and AI
The promises of 5G are now being realized through deployments around the world, and Ziad considers mmWave to be a game changer due to its ultra-low latency.
At the same time, the use of AI is also taking off both at the cloud and at the edge. And 5G allows for AI inference to be distributed to different parts of the network. At the same time, AI is designed to make communication technology (e.g., modems) more efficient with intelligent signal handling in complex conditions.
Powerful AI also has to be power efficient. There are key features of the Snapdragon 8 Gen 1 Mobile Platform architecture that can contribute to power reductions of up to three times that of the previous generation Snapdragon mobile platforms.
Developers can start with a rich AI software stack including the Qualcomm® Neural Processing SDK for artificial intelligence (AI) that provides a high-level pipeline for machine learning (ML) models. In addition, developers can also use our AI Model Efficiency Toolkit (AIMET), which provides advanced model quantization and compression techniques for trained neural network models.
IoT devices help allow for data collected at the edge to be processed locally or in the cloud. Bit IoT, where multiple devices collaborate, can be coordinated to provide different levels of intelligence. Similarly, federated learning can help developers with the option to perform training at the edge.
Another highlight for IoT is always-on AI. Always-on AI is expanding to encompass and fuse additional streams like the camera, sensors, etc., to provide new types of functionality (e.g., disable certain functions for the driver of a car).
Check it Out!
You can access the webinar along with a podcast of the presentation at this link. To read the complete blog post, Accelerating Distributed AI Applications, please visit Qualcomm Developer Network.
Snapdragon and Qualcomm Neural Processing are products of Qualcomm Technologies, Inc. and/or its subsidiaries. AIMET is a product of Qualcomm Innovation Center, Inc.