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ABC: An Academic Industrial-Strength Verification Tool
Open accessAbstract
ABC is a public-domain system for logic synthesis and formal verification of binary logic circuits appearing in synchronous hardware designs. ABC combines scalable logic transformations based on And-Inverter Graphs (AIGs), with a variety of innovative algorithms. A focus on the synergy of sequential synthesis and sequential verification leads to improvements in both domains. This paper introduces ABC, motivates its development, and illustrates its use in formal verification.
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Cited by
Genetic circuit design automation
Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D., & Voigt, C. A. (2016) · Science · doi:10.1126/science.aac7341 · Cited by 1,020
Machine Learning for Electronic Design Automation: A Survey
Huang, G., Hu, J., He, Y., Liu, J., Ma, M., Shen, Z., Wu, J., Xu, Y., Zhang, H., Zhong, K., et al. (2021) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3451179 · Cited by 341
Combining Induction, Deduction, and Structure for Verification and Synthesis
Seshia, S. A. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2471838 · Cited by 38
Metadata
- Authors
- Brayton, R., & Mishchenko, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 24-40
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1528837436
- Cited by
- 944
- References
- 0
- FWCI
- 17.55
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: ABC: An Academic Industrial-Strength Verification Tool. https://app.cochat.ai/papers/10.1007/978-3-642-14295-6_5
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- Brayton, R., & Mishchenko, A. (2010). ABC: An Academic Industrial-Strength Verification Tool. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-14295-6_5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
