GitHub
github.com › mit-han-lab › tinyml
GitHub - mit-han-lab/tinyml · GitHub
The TinyML project aims to improve the efficiency of deep learning AI systems by requiring less computation, fewer engineers, and less data, to facilitate the giant market of edge AI and AIoT.
Author mit-han-lab
Harvard University
pll.harvard.edu › course › fundamentals-tinyml
Fundamentals of TinyML | Harvard University
March 23, 2026 - What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible.
Interested in TinyML, where to start?
Hello! Looks like we’re in the same boat :) I started by auditing the EdX course “Fundamentals of TinyML” and from there learnt about LiteRT. My first real project is using small language models on Android S24 devices via Google’s AI Edge SDK. They have a bring-your-own-model option as well. I’ve also recently joined a few other professional communities online, the Edge AI discord channel (fka TinyML foundation) More on reddit.com
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February 15, 2026Run TinyML AI Models on ESP32: Complete Guide with Voice Command Recognition Project
Ask it to give you list of several real-life applications More on reddit.com
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June 14, 2025TinyML is a growing field in Machine Learning where models are deployed on microcontrollers. I wrote a blog on it. I hope you find it useful.
Alright everyone, BIG DATA is over, we’re all switching to MICRO DATA.
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November 13, 2020[N] Course on TinyML
amazing course More on reddit.com
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November 21, 2020What devices can run tinyML models?
tinyML models run on microcontrollers and other low-power edge devices at the smallest end of the spectrum, typically operating with milliwatt power budgets. MATLAB and Simulink support a wide range of popular microcontroller platforms through partnerships with semiconductor companies.
mathworks.com
mathworks.com › discovery › tinyml.html
tinyML - MATLAB & Simulink
How is tinyML different from traditional machine learning?
tinyML brings AI to the edge of a networked system for real-time, low-latency inference on low-power devices, while traditional machine learning typically relies on cloud connectivity and powerful servers. Unlike broader Embedded AI, tinyML specifically targets the smallest devices with milliwatt power budgets.
mathworks.com
mathworks.com › discovery › tinyml.html
tinyML - MATLAB & Simulink
What optimization techniques are used in tinyML?
Common optimization techniques include quantization, pruning, projection, and data type conversion to reduce memory and computational requirements without sacrificing significant accuracy. These techniques enable efficient execution on low-power devices while maintaining acceptable model performance.
mathworks.com
mathworks.com › discovery › tinyml.html
tinyML - MATLAB & Simulink
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Computer Vision with TinyML (Edge AI + CNNs) | Deploy Machine ...
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What Is Tiny ML? A Simple Explanation For Anyone - YouTube
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What is Tiny ML? ( Everything You Need to Know!) - YouTube
TinyML Explained: Running Machine Learning on Edge Devices
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What is TinyML? - YouTube
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TinyML | 2026 | (1) Introduction: What is TinyML? - YouTube
MathWorks
mathworks.com › discovery › tinyml.html
tinyML - MATLAB & Simulink
Tiny machine learning (tinyML) is a subset of machine learning focused on the deployment of models to microcontrollers and other low-power edge devices. It brings AI to the edge of a networked system, enabling real-time, low-latency, and ...
Google
sites.google.com › g.harvard.edu › tinyml-fall2020 › home
TinyML
Tiny Machine Learning (TinyML) is an introductory course at the intersection of Machine Learning and Embedded IoT Devices.
area of machine learning that focuses on deploying and running models on low-power, resource-constrained embedded systems such as microcontrollers and edge devices
Wikipedia
en.wikipedia.org › wiki › TinyML
TinyML - Wikipedia
May 1, 2026 - TinyML (short for tiny machine learning) is an area of machine learning that focuses on deploying and running models on low-power, resource-constrained embedded systems such as microcontrollers and edge devices. TinyML supports on-device inference with low latency and minimal reliance on cloud ...
Medium
medium.com › @sucheta963 › tinyml-running-deep-learning-models-on-microcontrollers-a1524a69e98c
🧠 TinyML: Running Deep Learning Models on Microcontrollers | by Sucheta Mandal | Medium
April 19, 2025 - TinyML (Tiny Machine Learning) refers to the deployment of machine learning models on tiny, power-efficient hardware devices like microcontrollers (MCUs) with severe memory, compute, and power constraints.
DataCamp
datacamp.com › blog › what-is-tinyml-tiny-machine-learning
What is TinyML? An Introduction to Tiny Machine Learning | DataCamp
February 13, 2023 - TinyML can be deployed on low-powered devices to continuously monitor machines for malfunctions and predict issues before they happen; this type of application boasts the potential to help businesses reduce costs that often arise from faulty machines.
Arm
arm.com › campaigns › arm-tinyml
Arm and Partners: tinyML Resources
tinyML is a community of researchers and industry engineers focused on bringing Machine Learning capabilities to microcontroller devices.
DFRobot
dfrobot.com › home › blog
Top 8 TinyML Frameworks and Compatible Hardware Platforms (TensorFlow Lite, Edge Impulse, PyTorch Mobile, etc.)
July 12, 2024 - TinyML is a branch of machine learning that focuses on creating and implementing machine learning models on low-power, small-footprint microcontrollers such as the Arduino.Machine Learning models require a significant amount of computing power.
TinyMLedu
tinymlx.github.io › courses
Take a Free Online Course or Teach Your Own! - TinyMLedu
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
SIGARCH
sigarch.org › tiny-machine-learning-the-future-of-ml-is-tiny-and-bright
TinyML: Why the Future of Machine Learning is Tiny and Bright | SIGARCH
May 6, 2024 - Early TinyML solutions consisted of simple ML models like decision trees and SVMs. But since then, the field has progressed to deep-learning-based TinyML models, typically small, efficient convolutional neural networks. However, there are still challenges to overcome.
Reddit
reddit.com › r/embedded › interested in tinyml, where to start?
r/embedded on Reddit: Interested in TinyML, where to start?
February 15, 2026 -
Hi, I'm an electrical engineering student and I have been interested lately in TinyML, I would love to learn about it and start making projects, but I am struggling a lot on how to start. Does anyone here work or have experience in the field that can give me some tips on how to start and what projects to do first?
Appreciate the help in advance
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Hello! Looks like we’re in the same boat :) I started by auditing the EdX course “Fundamentals of TinyML” and from there learnt about LiteRT. My first real project is using small language models on Android S24 devices via Google’s AI Edge SDK. They have a bring-your-own-model option as well. I’ve also recently joined a few other professional communities online, the Edge AI discord channel (fka TinyML foundation)
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You need to know Python, linear algebra, statistics, and computer architecture before you can really make use of nuanced ML libraries
Harvard Online
harvardonline.harvard.edu › program › professional-certificate-in-tiny-machine-learning-tinyml
Professional Certificate in Tiny Machine Learning (TinyML) | Harvard Online
You will learn about the emerging ... TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of tiny devices and embedded systems....
O'Reilly
oreilly.com › library › view › tinyml › 9781492052036
TinyML [Book]
December 16, 2019 - The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Authors Pete WardenDaniel Situnayake
Published 2019
Pages 504
Seeed Studio
wiki.seeedstudio.com › tinyml
TinyML | Seeed Studio Wiki
May 28, 2024 - TinyML is a field of study in Machine Learning and Embedded Systems that explores machine learning on small, low-powered microcontrollers, enabling secure, low-latency, low-power and low-bandwidth machine learning inferencing on edge devices.
Seeed Studio
seeedstudio.com › home › everything about tinyml – basics, courses, projects & more!
Everything About TinyML – Basics, Courses, Projects & More! - Latest News from Seeed Studio
February 21, 2024 - TinyML is one of the hottest trends in the embedded computing field right now, with 2.5 billion TinyML-enabled devices estimated to reach the market in the next decade and a projected market value exceeding $70 billion in just five years. If you want to get in on what all the excitement is about but aren’t sure where to start, this one-stop guide is specifically for you.
edX
edx.org › learn › tinyml
Best Online TinyML Courses and Programs | edX
TinyML, short for Tiny Machine Learning, refers to the deployment of machine learning models on resource-constrained devices, such as microcontrollers and embedded systems.
STMicroelectronics
st.com › content › st_com › en › campaigns › educationalplatforms › tinyml-and-efficient-deep-learning.html
tinyML and Efficient Deep Learning - STMicroelectronics - STMicroelectronics
He proposed the “Deep Compression” technique including pruning and quantization that is widely used for efficient AI computing, and “Efficient Inference Engine” that first brought weight sparsity to modern AI chips. He pioneered the TinyML research that brings deep learning to IoT devices, enabling learning on the edge (appeared on MIT home page).
Imagimob
imagimob.com › blog › what-is-tinyml
What is tinyml? - Learn More - Imagimob
February 18, 2024 - tinyML aka tiny ml is an abbreviation for tiny machine learning and means that machine learning algorithms are processed locally on edge devices.
XenonStack
xenonstack.com › blog › mlops-for-scaling-tinyml
What is TinyML? A Comprehensive Guide
December 2, 2024 - TinyML transforms machine learning (ML) in low-power devices to continuously monitor machine malfunctions and predict problems before they occur.