Neural Circuit Synthesis from Specification Patterns

• URL: http://arxiv.org/abs/2107.11864v1
• Date: Sun, 25 Jul 2021 18:17:33 GMT
• Title: Neural Circuit Synthesis from Specification Patterns
• Authors: Frederik Schmitt, Christopher Hahn, Markus N. Rabe and Bernd Finkbeiner
• Abstract summary: We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications. 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. We show that hierarchical Transformers trained on this synthetic data solve a significant portion of problems from the synthesis competitions.
• Score: 5.7923858184309385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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