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- W3211914908 abstract "We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufficient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i.e., pairs of specifications and circuits implementing them. We ensure that this synthetic data is sufficiently close to human-written specifications by mining common patterns from the specifications used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study." @default.
- W3211914908 created "2021-11-22" @default.
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- W3211914908 date "2021-12-06" @default.
- W3211914908 modified "2023-09-24" @default.
- W3211914908 title "Neural Circuit Synthesis from Specification Patterns" @default.
- W3211914908 hasPublicationYear "2021" @default.
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