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You can get a lot of innovation out of running machine learning inference on mobile devices, but what if you could also train your models on mobile devices? What would you invent if you could fine-tune your models at the network edge?
Were you able to attend this year’s AWS re:Invent from November 29 – December 3?
Qualcomm AI Research showcases its cutting-edge advancements in machine learning
Cooking with Snapdragon is a video on which we recently collaborated with WIRED Brand Lab, and published on
The 2021 ARM DevSummit was held from October 19 to 21, 2021.
The Qualcomm AI Developer Conference was held in September 2021 in Chengdu, China, and the theme this year was presenting a new era of the intelligent interconnection of technologies. Heng Xu, Sr.
Data is at the heart of modern business, providing real-time insight and control over day-to-day operations. This is facilitated by the explosion of the Internet of Things (IoT), with billions of devices collecting zettabytes of data . As IoT grows, so does the amount of data to be processed....
To run neural networks efficiently at the edge on mobile, IoT, and other embedded devices, developers strive to optimize their machine learning (ML) models' size and complexity while taking advantage of hardware acceleration for inference.
The term image processing encompasses many different tasks, including computational photography, computer vision algorithms, and even basics like image compression.
Edge devices are playing a key role in IIoT, Indus
In his recent webinar, Accelerating Distributed AI Applications, Ziad Asghar, our Vice President, Product Management, Qualcomm Technologies, Inc., gave an insightful and pragma
Centralized machine learning (ML) is the ML workflow that most of us are familiar with today, where training is allocated to powerful servers which update model parameters using large datasets.
Start and stop video recording through an Azure IoT hub. Send the status of your camera recordings to the AWS IoT console. Use GStreamer plug-ins to record [email protected], [email protected] and 1080p video streams via TCP.
New opportunities for mobile game developers to provide heightened levels of immersion are being created at the intersection of television, cinema, and videogames. It becomes even more compelling when these transmedia experiences are combined with 5G, pervasive gaming, and AI.
Are you still running your artificial intelligence workloads in the cloud? That may make sense for training your models, but if your applications depend on techniques like person detection and pose estimation to name a few, then it’s time you looked into on-device AI.
Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries.
Qualcomm Technologies contributes Hexagon DSP improvements to the open source Apache TVM community to scale AI


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