Results for: Edge Computing

Five IoT Home Projects You Can Build

Whether you’re a professional or hobbyist developer, you likely have some side-projects to help you discover new technology, learn how it works, or build solutions to hopefully make life easier. With...

ML training at the edge: Training on mobile devices

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-...

NeurIPS 2021: Discover our latest breakthroughs in AI

Qualcomm AI Research showcases its cutting-edge advancements in machine learning The pace of machine learning advancement continues at an astounding rate, and it’s that time of year again for AI...

Inspiration for Gamifying IoT Applications

Gamifying your app can potentially increase user engagement and incentivize your users to reach their goals as well as those of your organization. Now, as developers and others see the benefits,...

Neural Network Optimization with AIMET

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...

5G, 8K video, up to 7 cameras and 15 TOPS of processing on a drone

How many times have you said, “If I had a drone with 5G, seven-camera concurrency, 8K video and 15 TOPS of processing, I could . . .” Whatever you’ve told yourself you could do with features like...

Training ML Models at the Edge with Federated Learning

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. The...


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