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You like running your machine learning (ML) workloads with the Qualcomm Adreno OpenCL ML SDK on Adreno GPUs.
The international embedded computing community marked its 20th anniversary with this year’s Embedded World conference in Nuremberg.
When you look at your robotics application, do you think of it as an “intelligent-edge use case?” Probably not, but that’s where it plays.
In many industrial settings, determining the current health of assets still involves a technician putting their ear to a machine to detect any ominous sound deviations.
Today at Microsoft Build 2022, Qualcomm Technologies is announcing that we are opening up our industry-leading Qualcomm AI Engine for Windows developers with the Qualcomm Neural Processing SDK for Windows.
Hands up if you were on a video call this week? And keep your hands up if you got distracted by someone on the call typing, their dog barking, their kids playing, or other background noise. You were not alone.
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.

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