Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures

Utilizing Machine Learning Techniques for Worst-Case Execution Time Estimation on GPU Architectures

The massive parallelism provided by Graphics Processing Units (GPUs) to accelerate compute-intensive 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 computin...

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Bibliographic Details
Main Authors: Vikash Kumar, Behnaz Ranjbar, Akash Kumar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Timing analysis
machine learning
WCET analysis
graphics processing unit
measurement-based approach
Online Access:https://ieeexplore.ieee.org/document/10474357/

Internet

https://ieeexplore.ieee.org/document/10474357/

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