Abstract: In the last few years, microcontrollers became more and more powerful, and many authors have started to use them for different machine learning projects. One of the most popular frameworks ...
Embedded AI combines machine learning with edge devices for local, real-time intelligence. Courses range from beginner to advanced, covering TinyML, signal processing, and deployment. Ideal for ...
With TensorFlow Lite for Microcontrollers, you can run machine learning models on resource-constrained devices. Want to learn more? You can use it with Edge Impulse for speech recognition on an ...
Artificial intelligence (AI) is evolving rapidly, and choosing the right edge AI accelerator is crucial. Let’s highlight a few key AI accelerators, comparing their features and performance, to help us ...
Infineon CY8CKIT-062S2-AI evaluation kit is a hardware platform built around the PSoC 6 family of MCUs and designed to help developers easily create and test edge AI applications. The dev board ...
Amity School student Saumya Chauhan hopes her object-detection device will help the visually challenged handle daily challenges more confidently Share Innovation and entrepreneurship have nothing to ...
All of the machine language stuff coming out lately doesn’t affect you if you are developing with embedded microcontrollers, right? Perhaps not. Microsoft Research India wants you to use their EdgeML ...
The challenge with TinyML is to extract the maximum performance/efficiency at the lowest footprint for AI workloads on microcontroller-class hardware. The TinyML-CAM ...
Abstract: Traditionally, original equipment manufacturers (OEMs) send device-specific over-the-air (OTA) packages to ensure the latest firmware, security patches, etc. With millions of IoT devices, ...
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