When Smartwatches Can Translate Sign Language in Real Time Have you ever thought that a chip the size of a fingernail could run face recognition? MIT Han Lab’s open-source TinyML technology is making these sci-fi scenarios a reality. This groundbreaking technology has been successfully deployed in over 100,000 IoT devices, with WIRED and MIT News … Read more
Deep learning dominates various fields and fundamentally changes human society. Efficiency is a key factor in democratizing deep learning and expanding its application scope. This has become increasingly important as Moore’s Law slows down and the pace of model size expansion accelerates. We need efficient algorithms and systems to help bridge this gap. In this … Read more
Click the card below to follow the “LiteAI” public account Hi, everyone, I am Lite. Recently, I shared the Efficient Large Model Full-Stack Technology from Article One to Nineteen, which includes content on large model quantization and fine-tuning, efficient inference for LLMs, quantum computing, generative AI acceleration, etc. The content links are as follows: Efficient … Read more
Are you eager to master the future of artificial intelligence? Are you curious about how to embed powerful machine learning algorithms into a tiny Arduino board? Then, you definitely cannot miss the exciting TinyML theme at the 2AI/ML DevFest workshop! This article will take you deep into this event, review its highlights, and let you … Read more
When the ESP32 Development Board Meets TinyML In the intersection of the Internet of Things and artificial intelligence, the TinyML-ESP32 project has emerged as a dark horse! Supported by the Black Walnut Laboratory, this open-source project maximizes the performance of the ESP32-WROOM-32 development board, integrating hardware such as gyroscopes, microphones, and LED light groups to … Read more
TinyML is quietly changing the landscape of industrial detection, and today we will introduce a project—tinyml-example-anomaly-detection—that not only demonstrates how to use Python to train two distinctly different machine learning models for detecting motor anomalies but also reveals the entire process from data collection to model deployment. This article will give you a comprehensive understanding … 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 Dr. Matthew Stewart from Harvard University states, tinyML is an emerging discipline that enables low-resource consumption and low-power machine learning algorithms on resource-constrained microcontrollers. … Read more
Click the card below to follow the “LiteAI” public account Hi, everyone, I am Lite. Recently, I shared the Efficient Large Model Full-Stack Technology from Part 1 to 19, including large model quantization, fine-tuning, efficient inference of LLMs, quantum computing, generative AI acceleration, etc. The content links are as follows: Efficient Large Model Full-Stack Technology … Read more
Click the card below to follow the “LiteAI” official account Hi, everyone, I am Lite. Recently, 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 inference for LLMs, quantum computing, generative AI acceleration, and more. The content links are as … Read more
Click the card below to follow the “LiteAI” public account Hi, everyone, I am Lite. Some time ago, I shared the Efficient Large Model Full-Stack Technology from Part One to Nineteen, which includes topics such as large model quantization and fine-tuning, efficient inference for LLMs, quantum computing, and generative AI acceleration. The content links are … Read more