Click the blue text to follow us Introduction In today’s Internet of Things era, the application of voice recognition technology is becoming increasingly widespread. This article will introduce how to deploy a TinyML voice recognition system and detail the process from data collection to model deployment. Getting Started with TinyML: Implementing TinyML Voice Recognition Using … Read more
This article is contributed by the community, author Wang Yucheng, ML&IoT Google Developers Expert, Chief Engineer of the Intelligent Lock Research Institute at Wenzhou University. Learn more: https://blog.csdn.net/wfing After discussing the previous chapters, we have understood the concept of TinyML, completed the simplest TinyML model and ran it on a microcontroller, yielding the most basic … Read more
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
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
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
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
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
Computer vision is often seen as an important development direction for AI. With the continuous maturation of the AI industry and the expansion of application scenarios, the advantages of computer vision have gradually emerged, leading to an increase in market demand. Because computer vision is an indispensable and important sector for giants to layout AI, … Read more
▲ For more exciting content, please click the blue text above and follow us! Last November, a piece of AI hardware called “AI Pin” took the tech world by storm. This device breaks away from tradition, eliminating the screen interface and relying entirely on voice and projection technology to interact with users, responding to short … Read more
The functionality of artificial intelligence (AI) has become crucial in various mobile devices. Particularly in 2024, AI PCs are being launched, marking it as the “Edge Device AI Era.” This article reviews the main AI PCs and processors released in the second half of 2024. The semiconductor industry in 2024 is expected to be fully … Read more