Posted by Josh Gordon, Developer Advocate
A new HarvardX TinyML course on edX.org
Prof. Vijay Janapa Reddi of Harvard, the TensorFlow Lite Micro team, and the edX online learning platform are sharing a series of short TinyML courses this fall that you can observe for free, or sign up to take and receive a certificate. In this article, I’ll share a bit about TinyML, what you can do with it, and the upcoming HarvardX program.
TinyML is one of the fastest-growing areas of Deep Learning. In a nutshell, it’s an emerging field of study that explores the types of models you can run on small, low-power devices like microcontrollers.
TinyML sits at the intersection of embedded-ML applications, algorithms, hardware and software. The goal is to enable low-latency inference at edge devices on devices that typically consume only a few milliwatts of battery power. By comparison, a desktop CPU would consume about 100 watts (thousands of times more!). Such extremely reduced power draw enables TinyML devices to operate unplugged on batteries and endure for weeks, months and possibly even years — all while running always-on ML applications at the edge/endpoint.
|TinyML powering a simple speech recognizer. Learn how to build your own here.|
Although most of us are new to TinyML, it may surprise you to learn that TinyML has served in production ML systems for years. You may have already experienced the benefits of TinyML when you say “OK Google” to wake up an Android device. That’s powered by an always-on, low-power keyword spotter, not dissimilar in principle from the one you can learn to build here.
The difference now is that TinyML is becoming rapidly more accessible, thanks in part to TensorFlow Lite Micro and educational resources like this upcoming HarvardX course.
TinyML unlocks many applications for embedded ML developers, especially when combined with sensors like accelerometers, microphones, and cameras. It is already proving useful in areas such as wildlife tracking for conservation and detecting crop diseases for agricultural needs, as well as predicting wildfires.
TinyML can also be fun! You can develop smart game controllers such as controlling a T-Rex dinosaur using a neural-network-based motion controller or enable a variety of other games. Using the same ML principles and technical chops, you could then imagine collecting accelerator data in a car to detect various scenarios (such as a wobbly tire) and alert the driver.
|Chrome’s T-Rex dinosaur controlled using TensorFlow Lite for Microcontrollers.|
Fun and games aside, as with any ML application— and especially when you are working with sensor data—it’s essential to familiarize yourself with Responsible AI. TinyML can support a variety of private ML applications because inference can take place entirely at the edge (data never needs to leave the device). In fact, many tiny devices have no internet connection at all.
More About the Short Courses
The HarvardX course is designed to be widely accessible to developers. You will learn what TinyML is, how it can serve in the world, and how to get started.
The courses begin with ML basics, including how to collect data, how to train basic models (think: linear regression), and so on. Next, they introduce deep learning basics (think: MNIST), then Tiny ML models for computer vision, and how to deploy them using TensorFlow Lite for Microcontrollers. Along the way, the courses cover case studies and important papers, and increasingly advanced applications.
In one workflow, you’ll build a TensorFlow model using Python in Colab (as always), then convert it to run in C on a microcontroller. The course will show how to optimize the ML models for severely resource-constrained devices (e.g., those with less than 100 KB of storage). And it includes various case studies that examine the challenges of deploying TinyML “into the wild.”
Take TinyML Home
We’re excited to work closely with Arduino and HarvardX to make this experience possible.
|Arduino is preparing a TinyML kit, especially for the course.|
An off-the-shelf TinyML kit from Arduino will be available to edX learners for purchase. It includes an Arm Cortex-M4 microcontroller with onboard sensors, a camera and a breadboard with wires—everything needed to unlock the initial suite of TinyML application capabilities, such as image, sound and gesture detection. Students will have the opportunity to invent the future.
We’ll feature the best student projects from the course right here on the TensorFlow blog.
We’re excited to see what you’ll create!