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FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

By Ali Mokhtari and others
Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency constraints of ML applications. The challenge... Show more
June 9, 2022
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FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems
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