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tinyML Talks webcast: Evolutionary Needs of TinyML
"Evolutionary Needs of TinyML"

Liang Shen
Sr. Director of Engineering

During the past decade, Deep Learning based AI technology not only becomes the predominant solutions for existing or new problems, also almost instantly deployed to various smart devices. In this talk, we start with a brief review on how power-efficient AI engine helped this new AI wave and effectively enabled billions of battery-powered devices; then, we touch the new trend: always-on or long-continuous-run AI use cases, which require optimal minimum power solution. We discuss some details of ultra-low-power AI solution and how it offers the improved quality for targeted use cases. With continuous evolution of new intelligent algorithms, this talk concludes with on-going challenges and some potential directions.

"tinyMLedu: widening access to tinyML education and resources"

Brian Plancher
Ph.D. Candidate
Harvard John A. Paulson School of Engineering and Applied Sciences

TinyML can be used to enrich courses across the STEM curriculum, ranging from machine learning to embedded systems, with exciting, hands-on projects. tinyMLedu is working to help widen access to such applied machine learning experiences by building an international coalition of researchers and practitioners. Through collaborations across academia and industry, we are working to develop and share high quality, open-access educational materials globally and provide global access to the requisite hardware and software resources. You can learn more about our efforts at tinyMLedu.org.

"tinyML4STEM: using tinyML for Neuroscience in K12"

Greg Gage
Backyard Brains

Combining tinyML and Neuroscience enables exciting, hands-on STEM education experiences for the K12 audience. In this talk we will describe our successful effort piloting this exciting collaboration through Backyard Brains this past summer.

Jan 13, 2022 08:00 AM in Pacific Time (US and Canada)

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