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
Source: Zhuangzhi This article introduces the thesis, recommended reading time 5 minutes. A new trend is to utilize learning paradigms on devices, bringing the end-to-end ML process closer to edge devices. Modern machine learning (ML) applications are often deployed in cloud environments to leverage the computing power of clusters. However, traditional cloud computing solutions cannot … Read more
In recent years, a main theme in the AI market has been edge AI—or more specifically, edge-side AI, and even further, TinyML.Competitors in this market include not only traditional MCU/MPU suppliers and IP providers but also many startups focused on edge AI chips. The advantages of edge AI technology have been discussed in many articles, … Read more
MIT (Massachusetts Institute of Technology) published a paper in 2018 titled “The Decline of General-Purpose Computing: Why the End of Moore’s Law and Deep Learning is Leading to Computing Fragmentation”. This paper predicted that as Moore’s Law slows down and the costs of cutting-edge semiconductor manufacturing processes rise, general-purpose computing would struggle to meet the … Read more