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
Image source: putilov_denis/stock.adobe.com Machine Learning (ML) is a very good tool for solving problems involving pattern recognition. ML algorithms can transform chaotic raw data into usable signals. The basic process is to generate a model based on data, and then use the model to predict outputs, achieving learning, reasoning, and decision-making without human interaction. However, … Read more
TinyML, or tiny machine learning, is used to implement machine learning on resource-constrained devices such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power yet remarkably powerful devices, then this book is for you. This book aims to increase the accessibility of TinyML applications, especially for professionals who lack the resources … Read more
If you are interested in running machine learning on embedded devices but are unsure how to get started, Google’s TensorFlow Lite team member Pete Warden will introduce how to build and run your own TinyML applications. This will include an overview of the different boards, software frameworks, and tutorials available to help you get started … Read more
Author: Luo Kui, Technical Expert, Alibaba Case Overview With the development of machine learning, TinyML has been working in your home, car, and even pocket. What is TinyML? It is a subfield of machine learning that includes algorithms, hardware, and software capable of analyzing sensor data and running on ultra-low power devices. For example, voice … Read more
Introduction Check out Why FPGA/ADC Communication Prefers GPMC Interface in Industrial Fields? to learn about TinyML~ Today, I will introduce several open-source projects related to TinyML. TinyML Cookbook https://github.com/PacktPublishing/TinyML-Cookbook Overview This book is about TinyML, a rapidly developing field at the unique intersection of machine learning and embedded systems, enabling AI applications on ultra-low-power devices … Read more
Introduction: This issue is the AI Briefing for 20210219, bringing you 8 news articles. The first wave after the Spring Festival is here, spring has come, and work resumes! New year, new atmosphere, and I hope everyone pays more attention to the community, where more exciting content awaits you! This article has a total of … Read more