Uso de GPUs en aplicaciones de tiempo real: Una revisión de técnicas para el análisis y optimización de parámetros temporales
DOI:
https://doi.org/10.4995/riai.2023.20321Palabras clave:
Sistemas embebidos, Sistemas de tiempo real, Planificación, Procesadores heterogéneos, GPUsResumen
La conducción autónoma despierta un interés cada vez mayor en la industria, no solo en el sector de la automoción, sino también en el transporte de personas o mercancías por carretera o ferrocarril y en entornos de fabricación más controlados. Los sistemas ciber-físicos que se están proponiendo para este tipo de aplicaciones requieren de una gran capacidad de cómputo (arquitecturas hardware con varios núcleos, GPUs, NPUs…) para poder atender y reaccionar a una múltiple y compleja cantidad de sensores (cámaras, radar, LiDAR, medida de distancia, etc.). Por otro lado, este tipo de sistemas debe atender a requisitos de seguridad funcional y también de tiempo real. Este último aspecto plantea retos en los que se está trabajando intensamente y en los que aún quedan muchas cuestiones por resolver. En este trabajo, se hace una revisión de la literatura más reciente del uso de arquitecturas heterogéneas con GPUs en aplicaciones de tiempo real. Estos trabajos proponen soluciones para la estimación de cotas de tiempos de ejecución y respuesta temporal, proponiendo diferentes estrategias de optimización destacando la mitigación de interferencia en la memoria.
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Derechos de autor 2023 Iosu Gomez Iturrioz, Unai Díaz de Cerio, Jorge Parra, Juan Maria Rivas, J. Javier Gutiérrez
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