Lightning Talk: Efficient Embedded Machine Learning Deployment on Edge and IoT Devices
There has been rapid growth in the use of machine learning (ML) software in emerging edge and IoT systems. ML software deployments enable analytics and pattern recognition for multi-modal data (e.g., audio, images/video, wireless signals, air quality) obtained from embedded sensors and transceivers....
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| Vydané v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 2 |
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| Jazyk: | English |
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IEEE
09.07.2023
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| Abstract | There has been rapid growth in the use of machine learning (ML) software in emerging edge and IoT systems. ML software deployments enable analytics and pattern recognition for multi-modal data (e.g., audio, images/video, wireless signals, air quality) obtained from embedded sensors and transceivers. However, resource constraints in edge and IoT platforms make it challenging to meet quality-of-service and real-time goals. The growing complexity of ML also exacerbates these issues. We discuss the challenges of ML software deployment in edge and IoT platforms, present strategies to ease deployment, and discuss case studies from the automotive, indoor navigation, and hardware/software co-design domains. |
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| AbstractList | There has been rapid growth in the use of machine learning (ML) software in emerging edge and IoT systems. ML software deployments enable analytics and pattern recognition for multi-modal data (e.g., audio, images/video, wireless signals, air quality) obtained from embedded sensors and transceivers. However, resource constraints in edge and IoT platforms make it challenging to meet quality-of-service and real-time goals. The growing complexity of ML also exacerbates these issues. We discuss the challenges of ML software deployment in edge and IoT platforms, present strategies to ease deployment, and discuss case studies from the automotive, indoor navigation, and hardware/software co-design domains. |
| Author | Pasricha, Sudeep |
| Author_xml | – sequence: 1 givenname: Sudeep surname: Pasricha fullname: Pasricha, Sudeep email: sudeep@colostate.edu organization: Colorado State University,Department of Electrical and Computer Engineering,Fort Collins,CO,United States |
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| Snippet | There has been rapid growth in the use of machine learning (ML) software in emerging edge and IoT systems. ML software deployments enable analytics and pattern... |
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| SubjectTerms | edge computing embedded software Image edge detection Indoor navigation IoT computing Machine learning model optimizations Software Transceivers Wireless communication Wireless sensor networks |
| Title | Lightning Talk: Efficient Embedded Machine Learning Deployment on Edge and IoT Devices |
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