Eyes on Edge

The tinyML Vision Challenge contest is closed now, check out the winners here.

About us


Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

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October 08, 2021

tinyML Vision Challenge – Winners

We are very excited to announce the winners of Eyes on Edge: tinyML Vision Challenge! First we would like to thank the 485 people/teams that participated in our inaugural challenge we held with Hackster.io.

Industry News

    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…