Software and Computational Systems Lab

Publications of year 2025

1. Zhengyang Lu, Po-Chun Chien, Nian-Ze Lee, and Vijay Ganesh. Algorithm Selection for Word-Level Hardware Model Checking (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2025. Keyword(s): Btor2 Funding: DFG-BRIDGE PDF
   Abstract

   We build the first machine-learning-based algorithm selection tool for hardware verification described in the Btor2 format. In addition to hardware verifiers, our tool also selects from a set of software verifiers to solve a given Btor2 instance, enabled by a Btor2-to-C translator. We propose two embeddings for a Btor2 instance, Bag of Keywords and Bit-Width Aggregation. Pairwise classifiers are applied for algorithm selection. Upon evaluation, our tool Btor2-Select solves 30.0% more instances and reduces PAR-2 by 50.2%, compared to the PDR implementation in the HWMCC'20 winner model checker AVR. Measured by the Shapley values, the software verifiers collectively contributed 27.2% to Btor2-Select's performance.
   BibTeX Entry

   @inproceedings{AAAI25, author = {Zhengyang Lu and Po-Chun Chien and Nian-Ze Lee and Vijay Ganesh}, title = {Algorithm Selection for Word-Level Hardware Model Checking (Student Abstract)}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence~(AAAI)}, pages = {}, year = {2025}, pdf = {https://www.sosy-lab.org/research/pub/2025-AAAI.Algorithm_Selection_for_Word-Level_Hardware_Model_Checking_Student_Abstract.pdf}, abstract = {We build the first machine-learning-based algorithm selection tool for hardware verification described in the Btor2 format. In addition to hardware verifiers, our tool also selects from a set of software verifiers to solve a given Btor2 instance, enabled by a Btor2-to-C translator. We propose two embeddings for a Btor2 instance, Bag of Keywords and Bit-Width Aggregation. Pairwise classifiers are applied for algorithm selection. Upon evaluation, our tool Btor2-Select solves 30.0% more instances and reduces PAR-2 by 50.2%, compared to the PDR implementation in the HWMCC'20 winner model checker AVR. Measured by the Shapley values, the software verifiers collectively contributed 27.2% to Btor2-Select's performance.}, keyword = {Btor2}, doinone = {Unpublished: Last checked: 2024-11-18}, funding = {DFG-BRIDGE}, }