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October 28-29, 2024 | Tokyo, Japan
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Monday October 28, 2024 16:40 - 17:20 JST
End point devices are resource constrained, either in terms of power, memory or communication capabilities - sometimes all three. However, being able to apply machine learning on these end point devices is possible and when applied strategically enables system wide efficiencies to be realized. This talk will explore the requirements and tradeoffs for such system to be considered when using the Zephyr RTOS and Tensorflow Lite for Embedded Microcontrollers projects.
Speakers
avatar for Kate Stewart

Kate Stewart

VP Dependable Embedded Systems, Linux Foundation
Kate Stewart works with the safety, security and license compliance communities to advance the adoption of best practices into embedded open source projects. Since joining The Linux Foundation, she has launched the ELISA and Zephyr Projects, as well as supporting other embedded projects... Read More →
Monday October 28, 2024 16:40 - 17:20 JST
Hall B (4)

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