Paper Title TGTM: TinyML-based Global Tone Mapping for HDR Sensors 1 IntroductionAdvanced Driver Assistance Systems (ADAS) that rely on multiple cameras are becoming increasingly popular in vehicle technology.However, traditional imaging sensors struggle to capture clear images in conditions with strong lighting contrasts, such as at the exit of tunnels, due to their limited dynamic range.Introducing … Read more
(Written by Silicon Labs) Artificial Intelligence (AI) and Machine Learning (ML) technologies are not only rapidly evolving but are also being innovatively applied to low-power microcontrollers (MCUs) to achieve edge AI/ML solutions. These MCUs are an essential part of many embedded systems, capable of supporting AI/ML applications due to their cost-effectiveness, high energy efficiency, and … Read more
Artificial Intelligence(AI) on edge devices is revolutionizing the field of embedded electronics by enabling advanced computing capabilities directly on low-power devices. Traditionally, neural networks required powerful hardware and abundant resources, but with the development of technologies like TinyML, inference can now be performed directly on devices even with limited computational resources. Deploying neural networks on … Read more
In the field of embedded systems, microcontrollers (MCUs) serve as core control units and are widely used in home appliances, industrial control, and the Internet of Things (IoT). However, traditional microcontroller development has a high barrier to entry and relatively fixed functions, limiting its potential in the era of intelligence. With the rapid development of … Read more
↑ ClickBlue text Follow the Extreme City platformAuthor丨AI Vision EngineSource丨AI Vision EngineEditor丨Extreme City Platform Extreme City Guide This article presents the TinyFormer framework for developing Transformers on resource-constrained devices. By implementing a minimal and efficient Transformer on MCUs, it introduces Transformers into the TinyML scenario. Experimental results on CIFAR-10 show that TinyFormer achieves 96.1% accuracy, … Read more
EdgeML (Edge Machine Learning) and TinyML (Tiny Machine Learning) are two important subfields that have rapidly developed in the field of artificial intelligence in recent years. They both aim to deploy machine learning models’ inference (and sometimes even training/fine-tuning) close to the data source (sensors, devices), rather than relying on cloud data centers. This brings … Read more
According to reports from Electronic Enthusiasts (by Zhou Kaiyang), as users, we have witnessed the evolution of artificial intelligence from products to functionalities, and this trend is now spreading to the edge. However, due to strict power consumption and computing requirements at the edge, some existing solutions perform poorly, leading to edge AI often being … Read more
On July 18, 2025, the AIRS 2025 Youth AI Maker Summer Camp successfully concluded. The five-day intensive study program included thematic lectures, experimental operations, group projects, and various other formats,guiding participants to gain a deeper understanding of the fundamentals and practical applications of artificial intelligence and robotics, enhancing their understanding of engineering problems and hands-on … Read more
Open-Source Inference Engines Currently, the mainstream and active open-source TinyML inference engines with over 1k stars on GitHub provide core support for implementing neural network model inference on MCUs. Arm CMSIS-NN/DSP (CMSIS-6) A function library designed specifically for Arm Cortex-M cores, providing efficient neural network (NN) and digital signal processing (DSP) core functions. https://github.com/ARM-software/CMSIS_6 Google … Read more
Abstract—The evolution from 5G to 6G networks highlights the strong demand for Machine Learning (ML), particularly for Deep Learning (DL) models, which have been widely applied in mobile networks and communications to support advanced services in emerging wireless environments such as smart healthcare, smart grids, autonomous driving, aerial platforms, digital twins, and the metaverse. With … Read more
