EDGE AI FOUNDATION
From sleep monitoring wearables to face detection models, tinyML is making a big impact on the way we can gather and apply data. As tinyML technologies and ecosystems are gaining more momentum and maturity, more and more applications are being developed and deployed in different verticals.
The 3rd Annual tinyML EMEA Innovation Forum, which took place in Amsterdam from June 26-28, recently wrapped up. One of the objectives of the event was to unite the tinyML EMEA Community to empower and accelerate Innovation and Partnerships. The event drew a diverse range of participants, speakers, and sponsors, all converging to engage in insightful discussions and share ideas about the most recent developments and promising future of energy-efficient machine learning at the edge – tinyML.
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…
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.
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…
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).
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…