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Model-Based Dynamic Scheduling for Multicore Signal Processing.

, , , and . J. Signal Process. Syst., 91 (9): 981-994 (2019)

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A design tool for high performance image processing on multicore platforms., , , and . DATE, page 1304-1309. IEEE, (2018)Geometry entrapment in Walk-on-Subdomains., , , and . Monte Carlo Methods Appl., 25 (4): 329-340 (2019)CRE2019 Special Issue Introduction IJHPCA., and . Int. J. High Perform. Comput. Appl., (2020)Characterization of AI Model Configurations for Model Reuse., , , , and . ECCV Workshops (4), volume 13804 of Lecture Notes in Computer Science, page 454-469. Springer, (2022)Model-based dynamic scheduling for multicore implementation of image processing systems., , , and . SiPS, page 1-6. IEEE, (2017)MIST: Microscopy Image Stitching Tool., , , , , and . BIBM, page 1757. IEEE Computer Society, (2015)CGMBE: a model-based tool for the design and implementation of real-time image processing applications on CPU-GPU platforms., , , , , and . J. Real Time Image Process., 18 (3): 561-583 (2021)A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows., , , , and . J. Signal Process. Syst., 89 (3): 457-467 (2017)AI Model Utilization Measurements For Finding Class Encoding Patterns., , , , , , , and . CoRR, (2022)Extending Hedgehog's Dataflow Graphs to Multi-node GPU Architectures., , , , and . WAMTA, volume 13861 of Lecture Notes in Computer Science, page 1-12. Springer, (2023)