ArduTFLite——Arduino-style TensorFlow Lite Micro library ArduTFLite library simplifies the use of TensorFlow Lite Micro on Arduino boards, providing a typical Arduino-style API. It avoids the use of pointers or other C++ syntax structures that are discouraged in Arduino sketches. ArduTFLite serves as a wrapper for the Chirale_TensorFlowLite library, which is a port of the official … Read more
Author: Sophia IoT Think Tank Original As generative artificial intelligence stirs a global technological wave, another, more “low-key” yet equally critical technological direction is quietly rising: Edge AI, or as it is popularly known this year, On-Device AI. If Edge AI focuses on the decentralization of computing resources, then On-Device AI primarily involves the direct … Read more
Hey, recently I’ve been tinkering with something fun — running machine learning on those tiny IoT devices! Seeing the number “1KB”, many people shake their heads: how is that possible? Indeed, a high-definition photo takes several MB, so where’s the magic that allows AI to fit into such a tiny space? Actually, TinyML is such … Read more
In recent years, artificial intelligence technology has developed rapidly, but its powerful computing capabilities often rely on cloud servers. This poses a significant challenge for resource-constrained embedded devices. However, the rise of TinyML (Tiny Machine Learning) technology brings new hope: enabling resource-limited microcontrollers to run deep learning models! This article will take you into the … Read more
1. Evolution of Edge AI Technology From TinyML microcontroller-level inference to NVIDIA Jetson GPU-accelerated computing, the edge AI technology stack achieves a balance between computing power and power consumption. This tutorial covers the entire link of model lightweighting → real-time inference → offline deployment, focusing on solving core challenges such as model compression, hardware heterogeneity, … Read more
Edge AI: Three Memory Compression Techniques for Deploying TinyML with MicroPython To be honest, when I first tried to run a neural network on the ESP32, I was almost driven to madness. 256KB of RAM? Are you serious? That 5MB model I trained on Colab was completely out of the question. However, after experimenting over … Read more
Paper Title: Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge DevicesPublication Date: December 2024Authors: Thanaphon Suwannaphong, Ferdian Jovan, Ian Craddock, Ryan McConvilleAffiliations: University of Bristol, University of AberdeenOriginal Link: https://arxiv.org/pdf/2412.09289Open Source Code and Dataset Link: https://github.com/AloeUoB/tinyML_indoor_localisation Introduction Typically, accurate indoor localization systems rely on large machine learning … Read more
📢 Breaking News:Only 4 days left until the deadline for the 2025 China Edge Computing Top 20 Company list registration! If you miss this moment, your company may miss the biggest exposure opportunity in edge computing this year!🚀 The edge computing community has learned that Qualcomm has recently acquired the edge AI startup Edge Impulse. … Read more
Author: Zach Shelby (Founder of Edge Impulse, Co-founder of Yunhe Capital)IoT Think Tank Original This is my 364th column article. Recently, the field of Tiny Machine Learning (TinyML) has made milestone progress, crossing an important watershed. The maturity and development potential of this technology will reach a new level. The most representative event was Qualcomm’s … Read more
Author: Peng Zhao (Founder of Zhici Fang, Co-Founder of Yunhe Capital)IoT Think Tank Original This is my 365th column article. In the previous article “Tech Giants Enter TinyML, Edge AI Reaches a New Turning Point,” I mentioned that the TinyML Foundation has undergone a rebranding and is now known as the Edge AI Foundation. Recently, … Read more