Reproduce “SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design” on OpenResearch

About this paper

Jian Zuo, Junzhe Liu, Xianyong Wang, Chen Liang, Navya Goli, Umamaheswara Rao Tida, Zhenge Jia, Zhaoyan Shen and 1 more

SysVCoder, developed by researchers from Shandong University and North Dakota State University, introduces a two-stage LLM-driven framework that systematically generates complex, multi-hierarchical system-level Verilog designs from natural language descriptions. It achieved superior generation quality on system-level benchmarks and demonstrated significant efficiency gains compared to state-of-the-art agent-based frameworks.

Key insights

• A structured General Intermediate Representation (GIR) is critical for bridging natural language specifications to complex system-level hardware designs, significantly improving generation quality and transparency.
• Combining GIR-specific instruction tuning with a two-stage generation process effectively enhances an LLM's capability to reason about and synthesize multi-hierarchical hardware structures.
• Domain-Specific Retrieval-Augmented Generation (DS-RAG) with rule-based alignment effectively grounds LLM outputs in domain knowledge, improving functional correctness without computationally intensive retraining.

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