In recent years, there has been an exponential growth in the size and complexity of System-on-Chip (SoC) designs targeting different specialized applications. The cost of an undetected bug in these systems is much higher than in traditional processors, as it may imply loss of property or life. Despite decades of research on simulation and formal methods for debugging and verification, the problem is exacerbated by the ever-shrinking time-to-market and ever-increasing demand to churn out billions of devices. In this work, we propose VeriBug, which leverages recent advances in deep learning (DL) to accelerate debugging at the Register-Transfer level (RTL) and generates explanations of likely root causes. Our experiments show that VeriBug can achieve an average bug localization coverage of 82.5% on open-source designs and a wide variety of injected bugs.

An Attention-based Framework for Bug Localization in Hardware Designs / Stracquadanio, Giuseppe; Medya, Sourav; Quer, Stefano; Pal, Debjit. - ELETTRONICO. - (2024), pp. 1-2. (Intervento presentato al convegno DATE 2024: Design, Automation and Test in Europe tenutosi a Valencia (ESP) nel 25-27 March 2024).

An Attention-based Framework for Bug Localization in Hardware Designs

Stracquadanio, Giuseppe;Quer, Stefano;
2024

Abstract

In recent years, there has been an exponential growth in the size and complexity of System-on-Chip (SoC) designs targeting different specialized applications. The cost of an undetected bug in these systems is much higher than in traditional processors, as it may imply loss of property or life. Despite decades of research on simulation and formal methods for debugging and verification, the problem is exacerbated by the ever-shrinking time-to-market and ever-increasing demand to churn out billions of devices. In this work, we propose VeriBug, which leverages recent advances in deep learning (DL) to accelerate debugging at the Register-Transfer level (RTL) and generates explanations of likely root causes. Our experiments show that VeriBug can achieve an average bug localization coverage of 82.5% on open-source designs and a wide variety of injected bugs.
File in questo prodotto:
File Dimensione Formato  
extended_abstract.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 413.02 kB
Formato Adobe PDF
413.02 kB Adobe PDF Visualizza/Apri
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/11583/2989448