Knowledge Distillation

Breaking Through the Three Major Challenges of Edge AI: How Large Models Achieve Efficient Inference and Decision-Making on IoT Devices (with Practical Code)

by
Breaking Through the Three Major Challenges of Edge AI: How Large Models Achieve Efficient Inference and Decision-Making on IoT Devices (with Practical Code)

With the explosive growth of IoT devices, traditional pure cloud or pure edge architectures can no longer meet the balance of real-time performance, privacy security, and computational cost. This article proposes an edge-cloud collaborative architecture based on popular open-source large models, achieving millisecond-level environmental perception and autonomous decision-making through model hierarchical deployment, dynamic task offloading, … Read more

Training Vision Foundation Models on ImageNet-1K

by
Training Vision Foundation Models on ImageNet-1K

Follow our official account to discover the beauty of CV technology This article mainly introduces our work accepted at the ICLR-2025 conference: Accessing Vision Foundation Models via ImageNet-1K. Paper link: https://arxiv.org/abs/2407.10366 Code link: https://github.com/BeSpontaneous/Proteus-pytorch Existing vision foundation models such as CLIP[1], DINOv2[2], and SynCLR[3] are typically trained on large datasets (CLIP-400M, DINOv2-142M, SynCLR-600M), which not … Read more

Smart 6G: Edge Deployment and Lightweight Networks

by
Smart 6G: Edge Deployment and Lightweight Networks

Table of Contents | 2023 Issue 3 Special Topic: Deep Integration of 6G Sensing and Computing 01Mobile Communications 2023 Issue 2Special Topic on “Key Technologies and Applications of 6G” Smart 6G: Edge Deployment and Lightweight Networks* Zhou Ziyao, Liu Qingling, Tao Jianying, Lin Yun (School of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang … Read more

Edge AI: Three Memory Compression Techniques for Deploying TinyML with MicroPython

by
Edge AI: Three Memory Compression Techniques for Deploying TinyML with MicroPython

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

New Breakthrough in TinyML! Efficient Indoor Localization Using Transformers and Mamba

by
New Breakthrough in TinyML! Efficient Indoor Localization Using Transformers and Mamba

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