In the rapidly evolving semiconductor industry, where research, design,
verification, and manufacturing are intricately linked, the potential of Large
Language Models to revolutionize hardware design and security verification is
immense. The primary challenge, however, lies in the complexity of hardware
specific issues that are not adequately addressed by the natural language or
software code knowledge typically acquired during the pretraining stage.
Additionally, the scarcity of datasets specific to the hardware domain poses a
significant hurdle in developing a foundational model. Addressing these
challenges, this paper introduces Hardware Phi 1.5B, an innovative large
language model specifically tailored for the hardware domain of the
semiconductor industry. We have developed a specialized, tiered dataset
comprising small, medium, and large subsets and focused our efforts on
pretraining using the medium dataset. This approach harnesses the compact yet
efficient architecture of the Phi 1.5B model. The creation of this first
pretrained, hardware domain specific large language model marks a significant
advancement, offering improved performance in hardware design and verification
tasks and illustrating a promising path forward for AI applications in the
semiconductor sector.

本文介绍了 Hardware Phi 1.5B，一种专门为半导体行业硬件领域设计的创新大型语言模型，通过使用中等数据集进行预训练，该模型在硬件设计和验证任务中展现出改进的性能，为半导体行业的人工智能应用指明了一个有希望的发展方向。

Hardware Phi-1.5B：一个大型语言模型编码硬件领域特定知识

Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain  Specific Knowledge

The security of computer systems typically relies on a hardware root of
trust. As vulnerabilities in hardware can have severe implications on a system,
there is a need for techniques to support security verification activities.
Assertion-based verification is a popular verification technique that involves
capturing design intent in a set of assertions that can be used in formal
verification or testing-based checking. However, writing security-centric
assertions is a challenging task. In this work, we investigate the use of
emerging large language models (LLMs) for code generation in hardware assertion
generation for security, where primarily natural language prompts, such as
those one would see as code comments in assertion files, are used to produce
SystemVerilog assertions. We focus our attention on a popular LLM and
characterize its ability to write assertions out of the box, given varying
levels of detail in the prompt. We design an evaluation framework that
generates a variety of prompts, and we create a benchmark suite comprising
real-world hardware designs and corresponding golden reference assertions that
we want to generate with the LLM.

本研究探讨了使用自然语言提示生成 SystemVerilog 声明（用于硬件安全），研究了一种流行的大型语言模型的能力，并设计了一个评估框架来生成各种提示并创建基准套件，该套件由真实硬件设计和相应的黄金参考声明组成。

利用 LLM 辅助生成硬件断言

LLM-assisted Generation of Hardware Assertions

Verifying the robustness of machine learning models against evasion attacks
at test time is an important research problem. Unfortunately, prior work
established that this problem is NP-hard for decision tree ensembles, hence
bound to be intractable for specific inputs. In this paper, we identify a
restricted class of decision tree ensembles, called large-spread ensembles,
which admit a security verification algorithm running in polynomial time. We
then propose a new approach called verifiable learning, which advocates the
training of such restricted model classes which are amenable for efficient
verification. We show the benefits of this idea by designing a new training
algorithm that automatically learns a large-spread decision tree ensemble from
labelled data, thus enabling its security verification in polynomial time.
Experimental results on publicly available datasets confirm that large-spread
ensembles trained using our algorithm can be verified in a matter of seconds,
using standard commercial hardware. Moreover, large-spread ensembles are more
robust than traditional ensembles against evasion attacks, while incurring in
just a relatively small loss of accuracy in the non-adversarial setting.

本文提出了一种叫做可验证学习的方法用以解决机器学习模型在测试阶段对抗攻击的安全验证问题，并提出了一种新的训练算法以自动学习人工决策树集合。在公开数据集上的实验结果证实，这种方法可以在标准的商业硬件上在几秒钟内验证其使用我们算法训练的大规模分布型集合，而且相对于传统集合，大规模分布型集合对于对抗攻击更具鲁棒性，而在非对抗环境下只会受到相对较小的精度损失。