Industry News

January 31, 2022

TinyML is bringing deep learning models to microcontrollers

Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill…

January 29, 2022

TinyML unlocks new possibilities for sustainable development technologies

In this article, we take a look at two tinyML projects that have the potential to make contributions towards sustainable development goals. While the first project is about revolutionising precision farming, the second one aims to create a network of low-cost sensors for mapping carbon emissions.

January 29, 2022

Klika Tech Joins tinyML Foundation

Klika Tech, an award-winning IoT and Cloud-native product and solutions development company, has joined the tinyML Foundation as a Strategic Partner to provide technical, cross-industry expertise as the organization advances Machine Learning for on-device data analytics and decision making at the edge.

The tinyML Foundation and its over…

November 10, 2021

Meet TinyML: The Latest Machine Learning Tech Having An Outsize Business Impact

As device sensors proliferate across every company’s value chain – from new product development through inspection, tracking, and delivery – tinyML is surfacing to provide actionable insights, transforming business as we know it. There are sound economic reasons for all this interest and activity. McKinsey researchers predict IoT will have a potential economic impact of US $4-11 trillion by 2025, identifying manufacturing as the largest vertical (US $1.2-3.7 trillion).

September 20, 2021

Why tinyML is such a big deal

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,…

September 17, 2021

Deploying Artificial Intelligence at the Edge: Key Takeaways from SEMI CTO Forum

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…

July 23, 2021

Himax Sponsors tinyML Vision Challenge to Foster tinyML Vision Development

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…

July 22, 2021

How TinyML is powering big ideas across critical industries

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…

June 07, 2021

Machine learning at the edge: TinyML is getting big

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. …

May 13, 2021

Tiny ML: The Next Big Opportunity In Tech

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.

March 26, 2021

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. …

February 16, 2021

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.]

November 03, 2020

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.

November 03, 2020

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.

October 30, 2020

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”.

January 11, 2020

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.

December 01, 2019

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.

October 31, 2019

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”.

July 12, 2019

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.

March 28, 2019

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.