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MCUNet and TinyML - Lecture 10

Offered By: MIT HAN Lab via YouTube

Tags

TinyML Courses Neural Networks Courses Embedded Systems Courses Microcontrollers Courses Quantization Courses Edge Computing Courses Neural Architecture Search Courses Model Compression Courses

Course Description

Overview

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Explore the world of MCUNet and TinyML in this comprehensive lecture from MIT's 6.5940 course. Delve into the intricacies of deploying machine learning models on microcontrollers and resource-constrained devices. Learn from Prof. Song Han as he discusses cutting-edge techniques for optimizing neural networks for embedded systems. Discover the challenges and solutions in bringing AI to edge devices, including memory constraints, power efficiency, and real-time processing. Gain insights into the latest advancements in TinyML, enabling AI applications on small, low-power devices. Understand the architecture and design principles of MCUNet, a framework for efficient deep learning on microcontrollers. Explore practical examples and use cases of TinyML in various industries, from IoT to wearable technology. Enhance your knowledge of efficient machine learning techniques and their applications in resource-limited environments.

Syllabus

EfficientML.ai Lecture 10 - MCUNet and TinyML (MIT 6.5940, Fall 2024, Zoom Recording)


Taught by

MIT HAN Lab

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