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Puneet Nemade

dblp:334/3742 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › hardware verification and test › debugging
bug localization
0.612022
Aries: A Semiformal Technique for Fine-Grained Bug Localization in Hardware Designs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022
Electronic design automation
hardware verification and test
0.612022
Aries: A Semiformal Technique for Fine-Grained Bug Localization in Hardware Designs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022
Electronic design automation
model checking
0.212022
Aries: A Semiformal Technique for Fine-Grained Bug Localization in Hardware Designs · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2022

Methods — techniques the papers use, named apart from their topics

model checking · 0.6binning · 0.6assertion mining · 0.6
YearPublicationVenuePosition
2022 Aries: A Semiformal Technique for Fine-Grained Bug Localization in Hardware Designs
abstract
Effective bug localization during verification is a challenging step in the development cycle of complex hardware designs. While meeting different coverage goals is possible in the verification process, yet bug localization cannot be directly related to such goals. We propose a two-step methodology to achieve fine-grained design bug localization. First, we obtain multiple error traces based on a failing property. Starting from an initial error trace, we employ model checking to generate supportive error traces that are utilized to mine important assertions. In the second step, we utilize these assertions for fine-grained design bug localization. The mapping of the assertions leads to specific regions in register transfer level descriptions that are highly probable to be the root cause of the design bug. Specifically, we devise a binning methodology to categorize multiple suspects in different bins that need to be investigated by the design engineer for arriving at the correction for corresponding bugs. Experiments on multiple designs illustrate the efficacy of the proposed methodology in comparison to previous work and state-of-the-art industrial tool.
Binod Kumar 0001, Vineesh V. S., Puneet Nemade, Masahiro Fujita 0004
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.3