麻省理工学院的课程“TinyML和高效深度学习计算”是一门专门针对在资源受限设备上部署深度神经网络所面临挑战的课程。它着重于实用技术,如模型压缩、修剪、量化和神经架构搜索,这些对于在计算能力有限的设备上实现人工智能应用至关重要。该课程以其实践性方法而著称,允许学生实施模型压缩技术,并在笔记本电脑上部署大型语言模...
最近学习一下 TinyML and Efficient Deep Learning Computing 课程,同时自己做了简单的笔记。腾讯文档 最近将持续更新文档,学习加油。
Deep leaning algorithms are resource-demanding. This talk will present techniques to reduce the computation recourse, human resource, and data resource for deep learning. First, I'll present MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the light-weight ...
In this project we’re going to employ a more efficient method and directly parse user utterances into actionable output in form of intent/slots. This tutorial will demonstrates how to use Wio Terminal to set up a simple gesture recognition machine learning demo with the help of TensorFlow Lite...
TinyML 算法的工作机制与传统机器学习模型几乎完全相同,通常在用户计算机或云中完成模型的训练。训练后处理是 TinyML 真正发挥作用之处,通常称为“深度压缩”(deep compression)。 图4 深度压缩示意图。来源:ArXiv 论文 模型蒸馏(Distillation) 模型在训练后需要更改,以创建更紧凑的表示形式。这一过程的主要实现技术包括...
TinyML 算法的工作机制与传统机器学习模型几乎完全相同,通常在用户计算机或云中完成模型的训练。训练后处理是 TinyML 真正发挥作用之处,通常称为“深度压缩”(deep compression)。 图4 深度压缩示意图。来源:ArXiv 论文 模型蒸馏(Distillation) 模型在训练后需要更改,以创建更紧凑的表示形式。这一过程的主要实现技术包括...
TinyML 算法的工作机制与传统机器学习模型几乎完全相同,通常在用户计算机或云中完成模型的训练。训练后处理是 TinyML 真正发挥作用之处,通常称为“深度压缩”(deep compression)。 图4 深度压缩示意图。来源:ArXiv 论文 模型蒸馏(Distillation) 模型在训练后需要更改,以创建更紧凑的表示形式。这一过程的主要实现技术包括...
These on-device ML models have partly been made possible by advances in techniques used to make neural networks compact and more compute- and memory-efficient. But they have also been made possible thanks to advances in hardware. Our smartphones and wearables now pack more computing power than ...
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory cmicrocontrollerdeep-learningcpppytorchcodegeneratorquantizationedge-computingneural-architecture...
Note: Although TinyMaix support multi architecture accelerate, but it still need more effort to balance size and speed. Features in design Support up to mobilenet v1, RepVGG backbone they are most common used, efficient structure for MCUs ...