• AI In the Cloud Vs. On the Hardware: Where Tiny ML Shines

  • 2022/08/25
  • 再生時間： 27 分
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AI In the Cloud Vs. On the Hardware: Where Tiny ML Shines

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  • Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) 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.

    Anders Hardebring, CEO and Co-Founder of Imagimob is helping companies imagine what they can do by bringing machine learning into embedded devices. Imagine audio classification without any data ever leaving the embedded device. Or sensing that a machine is about to break and turn it off in a few milliseconds. Even when the connection is down.

    Tune in for a deep dive into the possibilities and be sure to visit https://www.imagimob.com/ to learn more.
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あらすじ・解説

Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) 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.

Anders Hardebring, CEO and Co-Founder of Imagimob is helping companies imagine what they can do by bringing machine learning into embedded devices. Imagine audio classification without any data ever leaving the embedded device. Or sensing that a machine is about to break and turn it off in a few milliseconds. Even when the connection is down.

Tune in for a deep dive into the possibilities and be sure to visit https://www.imagimob.com/ to learn more.
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