Constructing behavioral-level chiplet models (e.g., SystemC) is crucial for early-stage heterogeneous architecture exploration. Traditional manual modeling is notoriously time-consuming and error-prone. Recently, Large Language Models (LLMs) have demonstrated immense potential in automating hardware code generation. However, existing LLM-assisted design frameworks predominantly target highly structured or well-defined design specifications. In practical engineering scenarios, raw datasheets typically encompass lengthy, complex, and highly unstructured information. Consequently, reliable code generation directly from these raw datasheets suffers from severe challenges, including context vanishing and logical hallucinations.To overcome this critical bottleneck, this paper proposes DS2SC-Agent(Datasheet-to-SystemC-Agent): the first end-to-end, fully automated generation pipeline that translates raw datasheets directly into SystemC chiplet models. This system establishes a highly efficient multi-agent collaborative framework. By decoupling the intricate modeling tasks, the proposed pipeline orchestrates a fully automated workflow encompassing unstructured long-document parsing, SystemC core code construction, testbench stimulus generation, and adaptive closed-loop debugging. We comprehensively evaluate the proposed framework on representative single-function chiplets across the analog, digital, and radio frequency (RF) domains--specifically, a Limiting Amplifier (LA), a Fast Fourier Transform (FFT) module, and a Power Amplifier (PA). The evaluation demonstrates that our pipeline seamlessly processes complex real-world datasheets to consistently generate functionally correct SystemC models. This provides a highly efficient and reliable paradigm for agile model library construction while drastically minimizing manual intervention.

翻译：构建行为级芯粒模型（如SystemC）对于早期异构架构探索至关重要。传统手工建模耗时且易出错。近年来，大语言模型（LLM）在硬件代码自动化生成方面展现出巨大潜力。然而，现有基于LLM的设计框架主要针对高度结构化或定义明确的设计规范。在实际工程场景中，原始数据手册通常包含冗长、复杂且高度非结构化的信息。因此，直接基于这些原始数据手册进行可靠的代码生成面临严峻挑战，包括上下文丢失与逻辑幻觉。为突破这一关键瓶颈，本文提出DS2SC-Agent（数据手册至SystemC智能体）：首个将原始数据手册直接转换为SystemC芯粒模型的端到端全自动生成流水线。该系统构建了高效的多智能体协作框架。通过解耦复杂的建模任务，所提出的流水线编排了涵盖非结构化长文档解析、SystemC核心代码构建、测试激励生成与自适应闭环调试的全自动化工作流。我们在模拟、数字与射频（RF）领域的代表性单功能芯粒上全面评估了该框架，具体包括限幅放大器（LA）、快速傅里叶变换（FFT）模块与功率放大器（PA）。评估表明，我们的流水线能无缝处理真实世界复杂数据手册，持续生成功能正确的SystemC模型。这为敏捷模型库构建提供了高效可靠的范式，同时大幅减少了人工干预。