TinyML Articles

Industry Experts Discuss Technology: Renesas Expands MCU/MPU Product Line to Meet New Edge AI Processing Needs

2026-05-21 by boardor

Daryl Khoo Vice President of Embedded Processing Marketing Division Artificial Intelligence (AI) applications at the IoT edge are redefining how interconnected devices collect, process, and analyze data across various consumer and industrial scenarios to achieve actionable decision outcomes. Unlike cloud AI servers, which must prioritize power consumption, data latency, and security management, AIoT pushes intelligent … Read more

From Perception to Cognition: The Synergy of TinyML and Edge AI Marks the Dawn of the Lightweight AI Industrial Era

2026-05-18 by boardor

Author:Peng Zhao (Founder of Zhici Fang and Co-founder of Yunhe Capital)IoT Think Tank Original This is my 387th column article. In the era of rapid development of AI technology, when will “edge intelligence” truly mature and be implemented? This is a question that many developers, enterprises, and industry observers have repeatedly pondered. In the past, … Read more

TinyML: Running AI Models on Microcontrollers with Just a Few KB of Memory – A Boon for Embedded Systems

2026-05-10 by boardor

Click the blue text to follow us Can you believe it? A chip the size of a fingernail, with only a few KB of memory, can now run AI models! Imagine this: a smart bracelet can analyze heart rate anomalies without needing to connect to the internet, drones can identify crop diseases in real-time over … Read more

The Technological Symbiosis of AI and Embedded Systems

2026-05-02 by boardor

The “Intelligence Paradox” of Embedded Systems Under the dual pressure of the slowing Moore’s Law and the exponential growth of the Internet of Things, embedded systems are facing historic challenges: How to achieve intelligence on hardware with limited resources (<1MB memory, mW-level power consumption)? Traditional views hold that AI will disrupt the embedded field. However, … Read more

Artificial Nose: Smart Electronic Nose with TinyML

2026-04-30 by boardor

Introduction: A Wonderful Olfactory Journey from Baking to Technology Do you know the joy of baking? The aroma of freshly baked bread, the rich scent of coffee… But sometimes, judging the freshness of ingredients and timing the baking process perfectly can be a challenge. This open-source project shares all the necessary information to create and … Read more

Unlocking The Embedded Power Of AI: A Deep Dive Into The 2019 AI/ML DevFest TinyML Workshop

2026-04-30 by boardor

Are you eager to integrate the powerful capabilities of artificial intelligence into embedded devices? Imagine enabling your Arduino board to ‘think’ and make decisions! The 2019 AI/ML DevFest workshop offered such an opportunity, themed around TinyML, igniting a mini AI revolution. This article will take you through this exciting event, showcasing the allure of TinyML … Read more

80FPS! 1KB RAM! The Amazing TinyML-CAM Real-Time Image Recognition Project!

2026-04-30 by boardor

Hello everyone, this is Juejin GitHub. With the rise of the Internet of Things and edge computing, the demand for deploying artificial intelligence (AI) applications on resource-constrained micro-devices is growing. However, traditional AI models often require a large amount of computing resources and memory, making it difficult to run on these devices. Today, we will … Read more

Lightweight Embedded TinyML: The Perfect Combination of ESP32 and MicroPython

2026-04-30 by boardor

TinyML is rapidly becoming a popular technology in the Internet of Things (IoT) field, allowing machine learning models to run on resource-constrained microcontrollers. This article introduces the tinyml-esp project, which demonstrates how to develop TinyML applications on the ESP32 using MicroPython, implementing posture recognition based on accelerometer and gyroscope data. Project Overview: Implementing TinyML on … Read more

TinyML-CAM: Embedded Image Recognition System at 80 FPS with 1KB RAM

2026-04-30 by boardor

In the era of the Internet of Things (IoT) and edge computing, TinyML technology is becoming increasingly important. TinyML aims to deploy machine learning (ML) models on resource-constrained devices, making it possible to perform ML inference on microcontrollers. Next, we introduce TinyML-CAM, an efficient image recognition system based on the ESP32 platform. By using TinyML-CAM, … Read more

TinyML on ESP32: Create Your Micro Machine Learning Tool in Just a Few Steps!

2026-04-30 by boardor

In recent years, artificial intelligence (AI) technology has developed rapidly, but the high power consumption and cost associated with high-performance hardware have limited its application on edge devices. TinyML has emerged, bringing the powerful capabilities of machine learning to resource-constrained microcontrollers like the ESP32. This article will take you deep into the tinyml-esp project, allowing … Read more