1. Self-Introduction and Board Introduction Hello everyone, I am “A Big Brother Rong”, and this time I am participating in the first issue of the second season of Funpack. The board used in this episode is the TinyML development board from Syntiant, a provider of deep learning solutions, which uses the NDP101 ultra-low power neural … Read more
TinyML is a miniature or small-scale artificial intelligence technology that can run on resource-constrained microcontrollers (MCUs) with features such as low latency, low power consumption, and low cost. It can perform inference tasks in AI such as keyword detection, anomaly detection, and object recognition. MCU Manufacturers Merging with AI Companies to Layout TinyML In May … Read more
Machine Learning (ML) is a vibrant and powerful field of computer science that permeates almost all digital devices we interact with, whether it’s social media, mobile phones, cars, or even household appliances. Artificial Intelligence (AI) is rapidly moving from the “cloud” to the “edge,” entering increasingly smaller IoT devices. The machine learning processes implemented on … Read more
Industry Insights and News Sharing Invites You to Submit/Recommend Articles Electric Family Supply and Demand Information Platform www.dd1j.com www.ev108.com Author | Matthew Stewart Translator | Gai Lei Planning | Chen Si A trend in artificial intelligence is rapidly shifting from the “cloud” to the “edge”. TinyML is the implementation of AI on microcontrollers at the … Read more
1. Introduction Voice recognition, as an important method of human-computer interaction, is gradually becoming one of the core functions of smart devices. However, traditional voice recognition systems often rely on cloud servers for audio data processing and analysis, which brings issues such as latency and privacy. TensorFlow Lite provides an efficient and fast solution for … Read more
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