DS2SC-Agent: A Multi-Agent Automated Pipeline for Rapid Chiplet Model Generation

By Yiwei Wu, Yifan Wu, Yunhao Xiong, Dengwei Zhao, Jiaxuan Shen, Jianfei Jiang, Guanghui He, Shikui Tu, and Yanan Sun
School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract

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.

Index Terms — Chiplet Model Generation, Large Language Model, Agent System

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