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
▲Click above Lei Feng Network Follow Zhang Yongqian stated that the contradiction between the data generated at the edge and the limited processing capacity of the backbone network is becoming increasingly prominent. To solve this contradiction, a combination of cloud, edge, and terminal is essential. Written by | Bao Yonggang The integration of AIoT is … Read more
With the explosive growth of information in modern society, more and more memory and AI computing are being used for information storage and computation. However, even with powerful cloud computing, it is challenging to fully meet the demands for data bandwidth and transmission speed. Additionally, as people’s emphasis on data privacy protection increases, edge AI … Read more
Every time a new industry term emerges, we media professionals face the challenge of understanding what a company aims to achieve, especially when they use these terms to fulfill their marketing goals. A recently popular term is Edge Artificial Intelligence (AI), or Edge AI. Due to the rapid development of the Internet of Things (IoT) … Read more
Recent advancements in artificial intelligence (AI), edge computing, and the adoption of Internet of Things (IoT) devices have converged to create opportunities for edge AI. This has opened new possibilities for edge AI that were previously unimaginable—from assisting radiologists in identifying diseases, to driving cars on highways, to helping us pollinate plants. The concept of … Read more
Recently at CES Asia, Dr. Huang Chang, co-founder and vice president of Horizon, was invited to give a keynote speech titled “Trends in Edge AI Computing” at the main forum of CES. In a one-hour speech, Dr. Huang shared his insights on the trends of edge computing in the AI era, discussing the changes in … Read more