TinyML

How to Implement TinyML? A Review of Efficient Neural Networks for Micro Machine Learning

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How to Implement TinyML? A Review of Efficient Neural Networks for Micro Machine Learning

Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices. This review provides an in-depth analysis of the advancements in efficient neural networks and the deployment methods of deep learning models for TinyML applications on ultra-low-power microcontrollers (MCUs). It first introduces neural networks along with their … Read more

Understanding tinyML: Machine Learning on MCUs

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Understanding tinyML: Machine Learning on MCUs

Author: C. J. Abate (USA) Translator: Jun Qian Machine Learning (ML), as a subset of Artificial Intelligence, has been widely applied in various fields, including atmospheric science and computer vision. As Harvard PhD Matthew Stewart states, tinyML is an emerging discipline that enables low-resource, low-power machine learning algorithms to run on resource-constrained microcontrollers. C.J. Abate: … Read more

Overview of TinyML: Progress and Future by MIT’s Han Song

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Overview of TinyML: Progress and Future by MIT's Han Song

Tiny Machine Learning (TinyML) is the new frontier of machine learning. By compressing deep learning models into billions of Internet of Things (IoT) devices and microcontrollers (MCUs), we expand the scope of AI applications and achieve ubiquitous intelligence. However, due to hardware limitations, TinyML faces challenges: the tiny memory resources make it difficult to accommodate … Read more

Advancing Edge AI with TinyML

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Advancing Edge AI with TinyML

We previously published an article titled “TinyML: The Next Wave of Artificial Intelligence Revolution“, and held several events related to machine learning. We believe everyone has a certain understanding of TinyML, so why did we choose TinyML? Why Choose TinyML? ✦ Artificial Intelligence (AI) is rapidly transitioning from the cloud to the edge, entering increasingly … Read more

Understanding the Explosive Growth of TinyML

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Understanding the Explosive Growth of TinyML

Author: Wu Nü Wang (Peng Zhao) IoT Think Tank Original Reprint must indicate the source and origin Introduction The most widely distributed IoT devices are often very small and have limited power. They serve as terminal hardware, collecting various data through embedded sensors; their computing power is limited and extremely sensitive to power consumption. Can … Read more

How to Use TinyML for Edge Computing in Embedded Systems

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How to Use TinyML for Edge Computing in Embedded Systems

Implementing edge computing in embedded systems is becoming increasingly popular, with several platforms beginning to support it, utilizing TinyML – compact machine learning. In addition to the eighth edition of Arduino Nano 33 BLE launched by our company, ESP32, QuickLogic’s QuickFeather, and PICO are also building an ecosystem around TinyML. We will gradually launch activities … Read more

Alternatives to Power-Hungry and Expensive AI in the Global South

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Alternatives to Power-Hungry and Expensive AI in the Global South

On February 20, 2025, Science published an article titled “What is tinyML? Alternatives to power-hungry and expensive AI in the Global South,” which points out that due to the high power consumption and cost of many AI models, researchers in Global South countries are increasingly adopting low-cost, low-power alternatives. This article shares the main content … Read more

AI Proliferation Brings New Challenges to Embedded Designers

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Click the title below “Guo Xin Nan Fang” for quick follow-upSource: TechSugar From monitoring and access control to smart factories and predictive maintenance, artificial intelligence (AI) built on machine learning (ML) models has become ubiquitous in industrial IoT edge processing applications. As this trend becomes widespread, the construction of AI-supported solutions has become “mainstream”—shifting from … Read more