Innovative Electronic Design Automation (EDA) solutions are important to meet
the design requirements for increasingly complex electronic devices. Verilog, a
hardware description language, is widely used for the design and verification
of digital circuits and is synthesized using specific EDA tools. However,
writing code is a repetitive and time-intensive task. This paper proposes,
primarily, a novel deep learning framework for training a Verilog
autocompletion model and, secondarily, a Verilog dataset of files and snippets
obtained from open-source repositories. The framework involves integrating
models pretrained on general programming language data and finetuning them on a
dataset curated to be similar to a target downstream task. This is validated by
comparing different pretrained models trained on different subsets of the
proposed Verilog dataset using multiple evaluation metrics. These experiments
demonstrate that the proposed framework achieves better BLEU, ROUGE-L, and chrF
scores by 9.5%, 6.7%, and 6.9%, respectively, compared to a model trained from
scratch.

本文提出一种基于深度学习的 Verilog 自动补全模型的训练框架，该框架通过将通用编程语言预训练模型与类似目标下游任务的数据集集成并进行微调来验证，实验结果表明，该框架相比于从头开始训练的模型，具有更好的 BLEU、ROUGE-L 和 chrF 评分。

一种 Verilog 自动完成的深度学习框架 —— 向设计和验证自动化

A Deep Learning Framework for Verilog Autocompletion Towards Design and  Verification Automation

Computer-aided translation (CAT), the use of software to assist a human
translator in the translation process, has been proven to be useful in
enhancing the productivity of human translators. Autocompletion, which suggests
translation results according to the text pieces provided by human translators,
is a core function of CAT. There are two limitations in previous research in
this line. First, most research works on this topic focus on sentence-level
autocompletion (i.e., generating the whole translation as a sentence based on
human input), but word-level autocompletion is under-explored so far. Second,
almost no public benchmarks are available for the autocompletion task of CAT.
This might be among the reasons why research progress in CAT is much slower
compared to automatic MT. In this paper, we propose the task of general
word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct
the first public benchmark to facilitate research in this topic. In addition,
we propose an effective method for GWLAN and compare it with several strong
baselines. Experiments demonstrate that our proposed method can give
significantly more accurate predictions than the baseline methods on our
benchmark datasets.

本文介绍了计算机辅助翻译 (CAT) 的核心功能自动补全，而之前的研究缺少针对单词级别自动补全的检测，并提供了第一个公共数据集进行检测以促进研究进展。作者提出一种有效的方法进行单词级别自动补全，并与几个强基线进行比较。实验表明，我们提出的方法可以比基线方法更准确地预测我们的基准数据集。