Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. Prototyping and optimizing PCIe devices for emerging scenarios is an ongoing challenge. Since Transaction Layer Packets (TLPs) capture device-CPU interactions, it is crucial to analyze and generate realistic TLP traces for effective device design and optimization. Generative AI offers a promising approach for creating intricate, custom TLP traces necessary for PCIe hardware and software development. However, existing models often generate impractical traces due to the absence of PCIe-specific constraints, such as TLP ordering and causality. This paper presents Phantom, the first framework that treats TLP trace generation as a generative AI problem while incorporating PCIe-specific constraints. We validate Phantom's effectiveness by generating TLP traces for an actual PCIe network interface card. Experimental results show that Phantom produces practical, large-scale TLP traces, significantly outperforming existing models, with improvements of up to 1000$\times$ in task-specific metrics and up to 2.19$\times$ in Frechet Inception Distance (FID) compared to backbone-only methods.

本研究针对现有生成模型无法提供适合PCIe设备设计的实际TLP追踪的问题，提出了一种新颖的框架“幻影”，通过引入PCIe特定约束，将TLP追踪生成视为生成性人工智能问题。实验结果表明，“幻影”在生成实际、规模较大的TLP追踪方面优于现有模型，性能提升可达1000倍，显著推动了PCIe硬件和软件开发的进展。

幻影：为实用领域特定外设追踪合成约束生成人工智能模型