Privacy and new functions will make TinyML big
By Stacey Higginbotham
Privacy and smart features that don’t depend on an app will likely drive the adoption of machine learning (ML) on constrained edge devices going forward. That was the message Zach Shelby, CEO of Edge Impulse, and I tried to convey when we sat on a virtual panel at the tinyML Summit this week. …
Machine Learning Is Giving Cancer Detection New Bionic Eyes
Machine learning analysis of images is being used to provide medical diagnosis. And portable solutions using vision at the edge provide solutions that efficient, lower cost, and more timely than clinical solutions. Professor Mohammed Zubair’s research is leading the way in detecting oral cancer. [Don’t miss Professor Zubair’s tinyML Talks on this topic too.]
TinyML Could Democratize AI Programming for IoT
Upgrading microcontrollers with small, essentially self-contained neural networks enables organizations to deploy efficient AI capabilities for IoT without waiting for specialized AI chips.
How TinyML Makes Artificial Intelligence Ubiquitous
TinyML is the latest from the world of deep learning and artificial intelligence. It brings the capability to run machine learning models in a ubiquitous microcontroller – the smallest electronic chip present almost everywhere.
Can artificial intelligence give elephants a winning edge?
Open-source developers and tech giants created the world’s most advanced elephant tracking collars.
“Sara Olsson, a Swedish software engineer who has a passion for the natural world created a tinyML and IoT monitoring dashboard”.
Why tinyML is a giant opportunity right now
The world is about to get a whole lot smarter. As the new decade begins, we’re hearing predictions on everything from fully remote workforces to quantum computing. However, one emerging trend is scarcely mentioned on tech blogs – one that may be small in form but has the potential to be massive in implication. We’re talking about microcontrollers.
tinyML book written by Pete Warden and Daniel Situnayake of Google
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size—small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Stanford University Seminar
Evgeni Gousev of Qualcomm and Pete Warden of Google participated in a panel at Stanford University seminar “Current Status of tinyML and the Enormous Opportunities Ahead”.
AI at the Very, Very Edge (EE Times)
When the TinyML group recently convened its inaugural meeting, members had to tackle a number of fundamental questions, starting with: What is TinyML? TinyML is a community of engineers focused on how best to implement machine learning (ML) in ultra-low power systems. The first of their monthly meetings was dedicated to defining the issue.
TinyML Sees Big Hopes for Small AI (EE Times)
SUNNYVALE, Calif. – A group of nearly 200 engineers and researchers gathered here to discuss forming a community to cultivate deep learning in ultra-low power systems, a field they call TinyML. In presentations and dialogs, they openly struggled to get a handle on a still immature branch of tech’s fastest-moving area in hopes of enabling a new class of systems.