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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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 2
Main Author: Pasricha, Sudeep
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
edge computing
embedded software
Image edge detection
Indoor navigation
IoT computing
Machine learning
model optimizations
Software
Transceivers
Wireless communication
Wireless sensor networks
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Summary: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.
DOI:10.1109/DAC56929.2023.10247845