Software-only diverse redundancy on GPUs for autonomous driving platforms
Visualitza/Obre
Cita com:
hdl:2117/187487
Tipus de documentText en actes de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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ProjecteCOMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
RYC-2013-14717 (MINECO-RYC-2013-14717)
RYC-2013-14717 (MINECO-RYC-2013-14717)
Abstract
Autonomous driving (AD) builds upon high-performance computing platforms including (1) general purpose CPUs as well as (2) specific accelerators, being GPUs one of the main representatives. Microcontrollers have reached ASIL-D compliance by implementing diverse redundancy with lockstep execution. However, ASIL-D compliant GPUs rely on either fully redundant lockstep GPUs (i.e. 2 GPUs), which doubles hardware costs, or fully redundant systems with a GPU and another accelerator, which virtually doubles design and validation/verification (V&V) costs. In this paper we analyze the degree of diversity achieved when implementing redundancy on a single GPU, showing that diverse redundancy is not achieved in many cases, and propose software strategies that guarantee achieving diverse redundancy for any kernel on systems using commercial off-the-shelf (COTS) GPUs, thus showing how to achieve ASIL-D compliance on a single COTS GPU in controlled scenarios.
CitacióAlcaide, S. [et al.]. Software-only diverse redundancy on GPUs for autonomous driving platforms. A: IEEE International Symposium on On-Line Testing and Robust System Design. "2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS 2019): 1–3 July 2019, Greece". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 90-96.
ISBN978-1-7281-2490-2
Versió de l'editorhttps://ieeexplore.ieee.org/document/8854378
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