We present a new high-level synthesis methodology for using large language
model tools to generate hardware designs. The methodology uses exclusively
open-source tools excluding the large language model. As a case study, we use
our methodology to generate a permuted congruential random number generator
design with a wishbone interface. We verify the functionality and quality of
the random number generator design using large language model-generated
simulations and the Dieharder randomness test suite. We document all the large
language model chat logs, Python scripts, Verilog scripts, and simulation
results used in the case study. We believe that our method of hardware design
generation coupled with the open source silicon 130 nm design tools will
revolutionize application-specific integrated circuit design. Our methodology
significantly lowers the bar to entry when building domain-specific computing
accelerators for the Internet of Things and proof of concept prototypes for
later fabrication in more modern process nodes.

我们提出了一种新的高级综合方法，利用大型语言模型工具生成硬件设计。该方法仅使用开源工具，不包括大型语言模型。通过一个案例研究，我们使用该方法生成了一个具有 wishbone 接口的置换同余随机数生成器设计。我们使用大型语言模型生成的仿真和 Dieharder 随机性测试套件验证了随机数生成器设计的功能和质量。我们记录了案例研究中使用的所有大型语言模型聊天记录、Python 脚本、Verilog 脚本和仿真结果。我们相信，我们的硬件设计生成方法与开源硅 130 纳米设计工具相结合，将彻底改变应用特定集成电路设计。我们的方法大大降低了构建物联网领域特定计算加速器和概念验证原型的门槛，以后可以在更现代的工艺节点进行制造。

利用高层次综合和大型语言模型生成、模拟和部署均匀随机数生成器的硬件设计

Leveraging High-Level Synthesis and Large Language Models to Generate,  Simulate, and Deploy a Uniform Random Number Generator Hardware Design

Although machine learning (ML) has been successful in automating various
software engineering needs, software testing still remains a highly challenging
topic. In this paper, we aim to improve the generative testing of software by
directly augmenting the random number generator (RNG) with a deep reinforcement
learning (RL) agent using an efficient, automatically extractable state
representation of the software under test. Using the Cosmos SDK as the testbed,
we show that the proposed DeepRNG framework provides a statistically
significant improvement to the testing of the highly complex software library
with over 350,000 lines of code. The source code of the DeepRNG framework is
publicly available online.

本文介绍 DeepRNG 框架，在随机数生成器中加入深度强化学习，通过简洁的状态表示显著改善了 Cosmos SDK 软件库的测试效果。

DeepRNG：面向深度强化学习辅助下的软件生成测试

DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software

In this paper we show that reporting a single performance score is
insufficient to compare non-deterministic approaches. We demonstrate for common
sequence tagging tasks that the seed value for the random number generator can
result in statistically significant (p < 10^-4) differences for
state-of-the-art systems. For two recent systems for NER, we observe an
absolute difference of one percentage point F1-score depending on the selected
seed value, making these systems perceived either as state-of-the-art or
mediocre. Instead of publishing and reporting single performance scores, we
propose to compare score distributions based on multiple executions. Based on
the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
present network architectures that produce both superior performance as well as
are more stable with respect to the remaining hyperparameters.

本文探讨了单一性能评分不足以比较非确定性方法的问题，并通过对序列标记任务的实验表明，随机数种子的选择可以导致最先进系统的 F1 得分的绝对差异，提出通过多次执行比较分数分布的方法，通过五项序列标记任务的实验，提出了产生优越性能和更稳定的网络体系结构。