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
Ceva’s NPU Core Targeting TinyML Workloads (Dylan McGrath, MPR, September 6, 2024) Ceva’s NeuPro-Nano is a licensed neural processing unit (NPU) designed for running TinyML workloads, providing up to 200 billion operations per second (GOPS) for power-constrained edge IoT devices. Compared to other competing NPU IP products aimed at IoT edge, NeuPro-Nano can function as … Read more
New Intelligence Report Editor: LRS [New Intelligence Guide] Enabling language models to think like humans. When ChatGPT was first released, it shocked us with its performance in conversations, so much so that it created the illusion that language models possess “thinking abilities”. However, as researchers delved deeper into language models, they gradually discovered that there … Read more
Click Follow us by clicking the blue text above This paper introduces a modular blueprint and the X1 framework aimed at advancing the development of accessible and scalable Reasoning Language Models (RLMs) by combining reinforcement learning and hierarchical reasoning strategies, simplifying the design and deployment of RLMs, enhancing efficiency, and reducing costs. Paper Introduction By … Read more