2024
DOI: 10.1109/access.2024.3379018
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Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures

Vikash Kumar,
Behnaz Ranjbar,
Akash Kumar

Abstract: The massive parallelism provided by Graphics Processing Units (GPUs) to accelerate computeintensive tasks makes it preferable for Real-Time Systems such as autonomous vehicles. Such systems require the execution of heavy Machine Learning (ML) and Computer Vision applications because of the computing power of GPUs. However, such systems need a guarantee of timing predictability. It means the Worst-Case Execution Time (WCET) of the application is estimated tightly and safely to schedule each application before i… Show more

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