Deep learning accelerator models described with software imperative languages are frequently used for their large-scale reliability analysis in order to overcome the prohibitive simulation times of logic-level and RTL models. However, they are faced with the challenge of preserving consistency between software-visible variables and faulty microarchitectural states. The goal of this work is to determine a suitable accelerator modelling that enables analysis without overloading the simulation engine. Toward this goal, the paper explores different accelerator modelling strategies featuring increasing levels of hardware visibility. They are compared in their capability to gain insights into the reliability of the multiply-and-accumulate (MAC) pipeline of an industry-standard deep learning accelerator from NVIDIA. Our results show that subtle microarchitectural details that are typically overlooked by competing approaches play a relevant role in determining accelerator reliability.

Exploring Software Models for the Resilience Analysis of Deep Learning Accelerators: the NVDLA Case Study

Veronesi A.
Primo
;
Dall'occo F.;Bertozzi D.
Supervision
;
Favalli M.;
2022

Abstract

Deep learning accelerator models described with software imperative languages are frequently used for their large-scale reliability analysis in order to overcome the prohibitive simulation times of logic-level and RTL models. However, they are faced with the challenge of preserving consistency between software-visible variables and faulty microarchitectural states. The goal of this work is to determine a suitable accelerator modelling that enables analysis without overloading the simulation engine. Toward this goal, the paper explores different accelerator modelling strategies featuring increasing levels of hardware visibility. They are compared in their capability to gain insights into the reliability of the multiply-and-accumulate (MAC) pipeline of an industry-standard deep learning accelerator from NVIDIA. Our results show that subtle microarchitectural details that are typically overlooked by competing approaches play a relevant role in determining accelerator reliability.
2022
9781665494311
mperative languages; Large-scales; Logic levels; Microarchitectural state; Modelling strategies; Scale reliability; Simulation engine; Simulation time; Software modeling
File in questo prodotto:
File Dimensione Formato  
DDECS2022_final.pdf

solo gestori archivio

Tipologia: Full text (versione editoriale)
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 218.26 kB
Formato Adobe PDF
218.26 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in SFERA 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/11392/2504331
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 7
social impact