Part 1: Overview In this tutorial, we will develop a machine vision application using machine learning technology on the STM32H747I Discovery development board. This tutorial is officially published by STMicroelectronics. Special thanks to the Edge Intelligence Lab for the original Chinese subtitles. The documents referenced in the video (also available for download from ST’s official … Read more
Introduction Check out Why FPGA/ADC Communication Prefers GPMC Interface in Industrial Applications? to learn about TinyML~ Today, I will introduce several open-source projects related to TinyML. TinyML Cookbook https://github.com/PacktPublishing/TinyML-Cookbook Overview This book is about TinyML, a rapidly evolving field that lies at the unique intersection of machine learning and embedded systems, enabling AI to be … Read more
Project Description This project uses the microphone built into the Syntiant TinyML development board to build a machine learning model through Edge Impulse, allowing the LED light on the board to display different effects based on Chinese voice commands. A total of four labels of sound data were trained, as detailed below. Label Keyword Action … Read more
This article is from Arduino Project Hub Author: Arduino “having11” Guy Prerequisites Like humans, plants can also get sick; for instance, the leaves of a plant may turn yellow or develop spots due to fungi or other pathogens. Thus, by harnessing the power of machine learning, we can scan the colors and then use them … Read more
Posted by: ArduinoTEAM — May 26th, 2020 Running Machine Learning (ML) on microcontrollers is one of the most exciting developments in recent years, allowing small battery-powered devices to detect complex movements, recognize sounds, or find anomalies in sensor data. To make it accessible for every embedded developer to build and deploy these models, we have … Read more
Click the blue text above to follow me Recently, while debugging a predictive maintenance system for an industrial client, I encountered an interesting problem: how to stably run a real-time inference system on an MCU like STM32H743, which has a frequency of 480MHz and 1MB of RAM? Today, I will share with you the technical … Read more
TinyML is a type of machine learning characterized by shrinking deep learning networks for use in micro hardware, primarily applied in smart devices. Ultra-low power embedded devices are “invading” our world, further promoting the proliferation of AI-driven IoT devices with the help of new embedded machine learning frameworks. FPGA has been applied across various fields … Read more
Today,the data processing architecture exhibits a “split” characteristic. The focus has shifted to “cloud” computing, which boasts massive scale and computational power, while “edge” computing places the processing right at the “front line”, connecting electronic devices with the real world. In the cloud, the volume of data stored is enormous, and processing must be queued … Read more
This article is contributed by the community, Author Wang Yucheng, ML & IoT Google Developers Expert, Chief Engineer of the Smart Lock Research Institute at Wenzhou University.Learn more: https://blog.csdn.net/wfing Introduction to TinyML 1. Overview Pete Warden and Daniel Situnayake co-authored a book introducing how to run ML on Arduino and ultra-low-power microcontrollers, TinyML: Machine Learning … Read more
Many innovations in today’s edge technology began with consumer electronics and mobile devices. The smartphones, tablets, cameras, smart speakers, drones, and other devices we use in our daily lives are actually various forms of AI edge devices. These devices are embedded with many different operating systems and optimizations for specific chips to enable them to … Read more