Click the card below to follow the “LiteAI” public account Hi, everyone, I am Lite. A while ago, I shared the Efficient Large Model Full-Stack Technology from Part 1 to Part 19, which includes content on large model quantization and fine-tuning, efficient LLM inference, quantum computing, generative AI acceleration, etc. The content links are as … Read more
Editor’s Note: The author, Jose Vicente Sáez Ibáñez, is a senior ML and software development engineer with international experience. He has been dedicated to researching the intersection of artificial intelligence and the Internet of Things (AIoT). Over the past few years, he has been deeply involved in the smart city industry across China, Spain, and … 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
Ms. Chloe Ma, Vice President of Business Development at Arm’s IoT Division Recently, Ms. Chloe Ma, Vice President of Business Development at Arm’s IoT Division, conducted an online interview with industry media, introducing the evolution of edge artificial intelligence (AI) and Arm’s new generation Ethos-U85 AI accelerator and the new IoT reference design platform Arm … Read more
According to MEMS Consulting, looking to the future, core chips, especially MEMS sensors, will remain an indispensable part of terminal devices; however, software is equally important in delivering value to users. Bosch Sensortec believes that sensor software will become increasingly intelligent, transforming MEMS sensors into more accurate and personalized systems that can help users cope … Read more