BOiLS: Bayesian Optimisation for Logic Synthesis Article Swipe
Antoine Grosnit
,
Cédric Malherbe
,
Rasul Tutunov
,
Xingchen Wan
,
Jun Wang
,
Haitham Bou Ammar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.06178
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.06178
Optimising the quality-of-results (QoR) of circuits during logic synthesis is a formidable challenge necessitating the exploration of exponentially sized search spaces. While expert-designed operations aid in uncovering effective sequences, the increase in complexity of logic circuits favours automated procedures. Inspired by the successes of machine learning, researchers adapted deep learning and reinforcement learning to logic synthesis applications. However successful, those techniques suffer from high sample complexities preventing widespread adoption. To enable efficient and scalable solutions, we propose BOiLS, the first algorithm adapting modern Bayesian optimisation to navigate the space of synthesis operations. BOiLS requires no human intervention and effectively trades-off exploration versus exploitation through novel Gaussian process kernels and trust-region constrained acquisitions. In a set of experiments on EPFL benchmarks, we demonstrate BOiLS's superior performance compared to state-of-the-art in terms of both sample efficiency and QoR values.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.06178
- https://arxiv.org/pdf/2111.06178
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226371348
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226371348Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.06178Digital Object Identifier
- Title
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BOiLS: Bayesian Optimisation for Logic SynthesisWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-11Full publication date if available
- Authors
-
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou AmmarList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.06178Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2111.06178Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2111.06178Direct OA link when available
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Computer science, Scalability, Artificial intelligence, Reinforcement learning, Machine learning, Set (abstract data type), Bayesian probability, Process (computing), Gaussian process, Gaussian, Programming language, Quantum mechanics, Database, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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