The Graphics Processing Units (GPUs) usage has extended from graphic applications to others where their high computational power is exploited (e.g., to implement Artificial Intelligence algorithms). These complex applications usually need highly intensive computations based on floating-point transcendental functions. GPUs may efficiently compute these functions in hardware using ad hoc Special Function Units (SFUs). However, a permanent fault in such units could be very critical (e.g., in safety-critical automotive applications). Thus, test methodologies for SFUs are strictly required to achieve the target reliability and safety levels. In this work, we present a functional test method based on a Software-Based Self-Test (SBST) approach targeting the SFUs in GPUs. This method exploits different approaches to build a test program and applies several optimization strategies to exploit the GPU parallelism to speed up the test procedure and reduce the required memory. The effectiveness of this methodology was proven by resorting to an open-source GPU model (FlexGripPlus) compatible with NVIDIA GPUs. The experimental results show that the proposed technique achieves 90.75% of fault coverage and up to 94.26% of Testable Fault Coverage, reducing the required memory and test duration with respect to pseudorandom strategies proposed by other authors.

On the Functional Test of Special Function Units in GPUs / Guerrero-Balaguera, Juan-David; Rodriguez Condia, Josie E.; Reorda, Matteo Sonza. - ELETTRONICO. - (2021), pp. 81-86. (Intervento presentato al convegno 2021 24th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS) tenutosi a Vienna nel April 7-9, 2021) [10.1109/DDECS52668.2021.9417025].

On the Functional Test of Special Function Units in GPUs

Guerrero-Balaguera, Juan-David;Rodriguez Condia, Josie E.;Reorda, Matteo Sonza
2021

Abstract

The Graphics Processing Units (GPUs) usage has extended from graphic applications to others where their high computational power is exploited (e.g., to implement Artificial Intelligence algorithms). These complex applications usually need highly intensive computations based on floating-point transcendental functions. GPUs may efficiently compute these functions in hardware using ad hoc Special Function Units (SFUs). However, a permanent fault in such units could be very critical (e.g., in safety-critical automotive applications). Thus, test methodologies for SFUs are strictly required to achieve the target reliability and safety levels. In this work, we present a functional test method based on a Software-Based Self-Test (SBST) approach targeting the SFUs in GPUs. This method exploits different approaches to build a test program and applies several optimization strategies to exploit the GPU parallelism to speed up the test procedure and reduce the required memory. The effectiveness of this methodology was proven by resorting to an open-source GPU model (FlexGripPlus) compatible with NVIDIA GPUs. The experimental results show that the proposed technique achieves 90.75% of fault coverage and up to 94.26% of Testable Fault Coverage, reducing the required memory and test duration with respect to pseudorandom strategies proposed by other authors.
2021
978-1-6654-3595-6
File in questo prodotto:
File Dimensione Formato  
Paper_SBST_SFU_camera_ready_polito.pdf

accesso aperto

Descrizione: Full paper published in the conference
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 625.2 kB
Formato Adobe PDF
625.2 kB Adobe PDF Visualizza/Apri
09417025.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.6 MB
Formato Adobe PDF
1.6 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2899592