Industry News

While machine-learning (ML) development activity most visibly focuses on high-power solutions in the cloud or medium-powered solutions at the edge, there is another collection of activity aimed at implementing machine learning on severely resource-constrained systems.

Known as TinyML, it’s both a concept and an organization — and it has acquired significant momentum over the last year or two.

“TinyML deployments are powering a huge growth in ML deployment,…

Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market, while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events like hurricanes and wildfires.

Today, AI algorithms are primarily run at large data centers…

Himax and tinyML Foundation share the same vision that tinyML technology can enable a new world with trillions of distributed intelligent devices that can accurately identify and classify what they see or sense in ultralow power and battery-powered features. To accelerate growth of the emerging tinyML field, the open knowledge exchange between developers and industries is of great importance. Hence, this tinyML Vision Challenge competition stimulates…

From cars and TVs to lightbulbs and doorbells. So many of the objects in everyday life have ‘smart’ functionality because the manufacturers have built chips into them.

But what if you could also run machine learning models in something as small as a golf ball dimple? That’s the reality that’s being enabled by TinyML…

Is it $61 billion and 38.4% CAGR by 2028 or $43 billion and 37.4% CAGR by 2027? Depends on which report outlining the growth of edge computing you choose to go by, but in the end it’s not that different.

What matters is that edge computing is booming. There is growing interest by vendors, and ample coverage, for good reason. …

Free white paper from ABI Research:

… TinyML aims to solve the issues of both cost and power efficiency by enabling data analytics performance on low-powered hardware with low processing power and small memory size, aided by software designed for small-sized inference workloads. It has the potential to revolutionize the future of the IoT.

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 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.]

Upgrading microcontrollers with small, essentially self-contained neural networks enables organizations to deploy efficient AI capabilities for IoT without waiting for specialized AI chips.

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.

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”.

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.

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.

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”.

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.

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.