Assertion-Based Validation using Clustering and Dynamic Refinement of Hardware Checkers

Post-silicon validation is a vital step in System-on-Chip (SoC) design cycle. A major challenge in post-silicon validation is the limited observability of internal signal states using trace buffers. Hardware assertions are promising to improve observability during post-silicon debug. Unfortunately, we cannot synthesize thousands (or millions) of pre-silicon assertions as hardware checkers (coverage monitors) due to hardware overhead constraints. Therefore, we need to select the most profitable assertions based on design constraints. However, the design constraints can also change dynamically during the device lifetime due to changes in use-case scenarios as well as input variations. Therefore, assertion selection needs to dynamically adapt based on changing circumstances. In this article, we propose an assertion-based post-silicon validation framework to address the above challenges. Specifically, this article makes two important contributions. We propose a clustering-based assertion selection technique that can select the most profitable pre-silicon assertions to maximize the fault coverage. We also present a cost-aware dynamic refinement technique that can select beneficial hardware checkers during runtime based on changing design constraints. Experimental evaluation demonstrates that our proposed pre-silicon assertion selection can outperform state-of-the-art assertion ranking methods (Goldmine and HARM). The results also highlight that our proposed post-silicon dynamic refinement can accurately predict area (less than 5% error) and power consumption (less than 3% error) of hardware checkers at runtime. This accurate prediction enables the identification of the most profitable hardware checkers based on design constraints.

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Publication Details

Journal
ACM Transactions on Design Automation of Electronic Systems
Published
2024-09-16
DOI
https://doi.org/10.1145/3696108
Primary Topic
Formal Methods in Verification
Type
article
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article

Assertion-Based Validation using Clustering and Dynamic Refinement of Hardware Checkers

Prabhat Mishra, Hasini Witharana, Sahan Sanjaya
ACM Transactions on Design Automation of Electronic Systems
Formal Methods in Verification
article

Assertion-Based Validation using Clustering and Dynamic Refinement of Hardware Checkers

Prabhat Mishra, Hasini Witharana, Sahan Sanjaya
article en

Abstract

Post-silicon validation is a vital step in System-on-Chip (SoC) design cycle. A major challenge in post-silicon validation is the limited observability of internal signal states using trace buffers. Hardware assertions are promising to improve observability during post-silicon debug. Unfortunately, we cannot synthesize thousands (or millions) of pre-silicon assertions as hardware checkers (coverage monitors) due to hardware overhead constraints. Therefore, we need to select the most profitable assertions based on design constraints. However, the design constraints can also change dynamically during the device lifetime due to changes in use-case scenarios as well as input variations. Therefore, assertion selection needs to dynamically adapt based on changing circumstances. In this article, we propose an assertion-based post-silicon validation framework to address the above challenges. Specifically, this article makes two important contributions. We propose a clustering-based assertion selection technique that can select the most profitable pre-silicon assertions to maximize the fault coverage. We also present a cost-aware dynamic refinement technique that can select beneficial hardware checkers during runtime based on changing design constraints. Experimental evaluation demonstrates that our proposed pre-silicon assertion selection can outperform state-of-the-art assertion ranking methods (Goldmine and HARM). The results also highlight that our proposed post-silicon dynamic refinement can accurately predict area (less than 5% error) and power consumption (less than 3% error) of hardware checkers at runtime. This accurate prediction enables the identification of the most profitable hardware checkers based on design constraints.

ACM Transactions on Design Automation of Electronic SystemsVol. 29(6)
University of Florida (US)
Openalex Percentile: Top 81%
Formal Methods in Verification
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