Classical redundancy-based fault detection techniques, such as Duplication with Comparison (DWC), rely on replicating thecomputation and comparing the replicas' output at a bit-wise granularity. In many application environments these costs are prohibitive,especially when applications are characterized by an intrinsic level of tolerance. This paper presents a novel fault-detection approachfor the specific context of image filtering. Peculiarity of the proposed approach is that it estimates the impact of the fault on the processed output,inorderto determine whether the image is usable or should be re-processed. Tolimit overheads, the proposed solution exploits Approximate Computing (AC), allowing the definition of disciplined AC strategies totrade-off between accuracy and costs. Core of our solution is the successful combination of Image Quality Assessment metrics and Machine Learning models to assess the visual impact of the fault in a lightweight manner. Extensive experimentalcampaigns demonstrate the effectiveness of the solution, achieving achieving a reduction in terms of execution time up to 44% with respect to the classical DWC, with a fault detection precision ranging from 94.58% to 96.70%, and recall ranging from 88.2% to 97.8% depending on the adopted level of approximation.

Fault Impact Estimation for Lightweight Fault Detection in Image Filtering

Bolchini C.;Boracchi G.;Cassano L.;Miele A.;Stucchi D.
2022

Abstract

Classical redundancy-based fault detection techniques, such as Duplication with Comparison (DWC), rely on replicating thecomputation and comparing the replicas' output at a bit-wise granularity. In many application environments these costs are prohibitive,especially when applications are characterized by an intrinsic level of tolerance. This paper presents a novel fault-detection approachfor the specific context of image filtering. Peculiarity of the proposed approach is that it estimates the impact of the fault on the processed output,inorderto determine whether the image is usable or should be re-processed. Tolimit overheads, the proposed solution exploits Approximate Computing (AC), allowing the definition of disciplined AC strategies totrade-off between accuracy and costs. Core of our solution is the successful combination of Image Quality Assessment metrics and Machine Learning models to assess the visual impact of the fault in a lightweight manner. Extensive experimentalcampaigns demonstrate the effectiveness of the solution, achieving achieving a reduction in terms of execution time up to 44% with respect to the classical DWC, with a fault detection precision ranging from 94.58% to 96.70%, and recall ranging from 88.2% to 97.8% depending on the adopted level of approximation.
Convolution
Fault Detection
Image processing
Image Quality Assessment Metrics
Neural Networks
File in questo prodotto:
File Dimensione Formato  
09309056.pdf

Accesso riservato

: Publisher’s version
Dimensione 3.26 MB
Formato Adobe PDF
3.26 MB Adobe PDF   Visualizza/Apri
TC2020_Final.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 3.3 MB
Formato Adobe PDF
3.3 MB Adobe PDF Visualizza/Apri

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/11311/1158407
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact