tinyML Foundation is a worldwide non-profit organization empowering a community of professionals, academia and policy makers focused on low power AI at the very edge of the cloud.
Way back at the inaugural tinyML Summit in March 2019 there began to form a community to bring AI workloads out of the clouds down to the point of impact in a sustainable and democratized way to run ML and AI workloads in highly resource constrained environments.
As the field of AI has evolved so quickly in the past few years – especially with leaps in generative AI, new semiconductor capabilities and architectures, new connectivity methods and technologies, and software orchestration tools and paradigms – the tinyML community has evolved as well, expanding its technical scope to include higher performance AI silicon, new virtualization and container based approaches, and new solutions that spans verticals from retail to healthcare to agriculture to industry and maritime.
We also have evolved to be an inclusive world-wide community of technology professionals, Professors and students from academia around the globe, and NGOs and governance bodies like AI for Good and the World Economic Forum.
The community engages virtually in realtime on platforms and communities like Discord as well as YouTube, LinkedIn, Twitch and Instagram, but especially in-person events which we host around the world in the Bay Area in the USA, Milan, Taipei, London Austin and more where the community empowers itself with new ideas, new partnerships and new innovations.
For more information
Please contact us at joinus@tinyml.org
The People
Pete BERNARD
Executive Director
tinyML Foundation
Rosina Haberl
Community Manager
tinyML Foundation
Elfego Solares
Events Manager
tinyML Foundation
Olga Goremichina
Operations Manager
tinyML Foundation
Evgeni GOUSEV
Chair Board of Directors
Qualcomm Research, USA
Zach SHELBY
Board of Directors - Member At Large
Edge Impulse
Didem ÜN ATES
Board of Directors - Member At Large
LotusAI Ltd
Vijay Janapa REDDI
Board of Directors - Member At Large
Harvard University
MLCommons