▼Click the card below to follow our public account for more exciting content▼Since the launch of my course, I have received some doubts from people who do not understand AI models: the biggest doubt is that “microcontrollers cannot run AI at all.”In the live class, I have taught everyone how to deploy TinyML neural networks … Read more
Edge AI and TinyML: Empowering Intelligent Connectivity In response to the four major challenges of cloud AI regarding latency, bandwidth, power consumption, and privacy, the AI field is undergoing a profound transformation from “cloud” to “edge”. Edge AI and TinyML, as the core driving forces of this transformation, are liberating intelligence from data centers and … Read more
AITinyML: The micro-intelligence revolution that is changing the world.1.AI is “shrinking“Based onTinyML (micro machine learning), sensors can runAI<1 milliwatt, cost<1 dollar, and memory of only a few hundred KB on microchips.For example: if cloudAI is the “central brain“—powerful but distant,TinyML is the “nerve endings“—tiny but ubiquitous.The core advantages of AI TinyML: mean thatAI will truly … Read more
Recently, Professor Wang Yang’s research group at the National Digital Fishery Innovation Center of China Agricultural University published a research paper titled “Real-time rapid visual fish disease detection system based on tiny machine learning” in the journal Aquaculture International. Article Overview Aquaculture International is an international academic journal focused on the field of aquaculture. The … Read more
Here are the current authoritative resources available regarding STM32Cube.AI (X-CUBE-AI): 1. Official Course Resources 1. ST Official Six-Lecture Course · Content: Covers the entire AI development process, including data collection, model conversion (using STM32Cube.AI), deployment verification, etc. · Features: · Designed specifically for embedded engineers, integrating practical STM32 hardware experience; · Includes live Q&A sessions … Read more
Hello everyone, welcome to this issue of the Zephyr RTOS Bulletin. 🚀 Top Story [ 👆 Replace with Elektor conference screenshot.png ] Zephyr’s “Global Coverage Day” concluded yesterday, with Rust emerging as the biggest winner! Yesterday (November 5), Zephyr held two major summits simultaneously online and in North America. According to leaked conference agendas and … Read more
Of course! STM32 AI is a very powerful and flexible technology combination, with the core idea of enabling resource-constrained embedded microcontrollers to efficiently run artificial intelligence algorithms, especially neural network models. In simple terms, STM32 AI brings AI from the cloud and powerful computing chips (like GPUs) down to the device itself, making devices smarter, … Read more
Harvard’s open-source AI systems engineering textbook teaches you how to implement smart speaker wake word detection using Arduino. It covers the complete process from data collection to edge deployment, with a model size of less than 100KB and a response time of under 50 milliseconds, to be published by MIT Press in 2026. Original text: … Read more
If you want to add gesture interaction to embedded devices but are worried about hardware limitations, the QuecPython open-source project might be just what you need — a real-time gesture recognition system based on TinyML technology, utilizing the MPU6500 six-axis sensor and a random forest model to achieve precise and lightweight gesture perception on the … Read more
Sharing Cutting-Edge Research on TinyML 01 Definition TinyML, or “Tiny Machine Learning,” refers to the technology, tools, and methodologies for running machine learning applications on ultra-low-power microcontrollers (MCUs) and other small computing devices. The core of TinyML is to compress and optimize trained machine learning models to fit the limited memory (typically KB-level SRAM, MB-level … Read more