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tinyML Talks: Empowering the Edge: Practical Applications of Embedded Machine Learning on MCUs
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"Empowering the Edge: Practical Applications of Embedded Machine Learning on MCUs"
Jongmin Lee
Machine Learning Architecture Engineer
NXP
Deploying ML models on edge devices offers several benefits such as reducing data traffic between the edge and the cloud, decreasing latency, and safeguarding privacy by avoiding the transmission of raw data to the cloud. Recent advancements on MCU hardware technology and ML model compression have enabled the deployment of lightweight ML models on MCUs with impressive performance. This talk introduces a few key examples of MCU-based ML applications, showcasing their practical implementation. Additionally, it highlights the significant acceleration of ML performance achievable with Neural Processing Unit enabled MCU.
Jongmin Lee
Machine Learning Architecture Engineer
NXP
Deploying ML models on edge devices offers several benefits such as reducing data traffic between the edge and the cloud, decreasing latency, and safeguarding privacy by avoiding the transmission of raw data to the cloud. Recent advancements on MCU hardware technology and ML model compression have enabled the deployment of lightweight ML models on MCUs with impressive performance. This talk introduces a few key examples of MCU-based ML applications, showcasing their practical implementation. Additionally, it highlights the significant acceleration of ML performance achievable with Neural Processing Unit enabled MCU.
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