Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Circuit Synthesis from Specification Patterns   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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The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat