SysVCoder: An LLM-Driven Framework for Systematic Generation of System-Level Design

Abstract

Recent advances in large language models (LLMs) have demonstrated strong potential in generating hardware designs using hardware description languages (HDLs) such as Verilog. However, existing LLM-based frameworks struggle to accurately capture the complexity of real-world architectural designs, particularly for large-scale systems with hierarchical, multi-level module instantiations. To address this issue, we present SysVCoder, an LLM-driven framework that enhances both the generation quality and efficiency of system-level design in Verilog. SysVCoder introduces a two-stage generation pipeline that leverages an intermediate representation to enable a more structured and accurate translation from natural language specifications to complex multi-module designs. Furthermore, we incorporate a rule-based alignment mechanism and a domain-specific retrieval-augmented generation strategy (DS-RAG) to enhance functional correctness by grounding LLM outputs in domain knowledge. We also present SysVDB, a comprehensive dataset comprising 60 system-level hardware designs along with their corresponding verification testbenches. Experimental results demonstrate that SysVCoder outperforms state-of-the-art frameworks such as CodeV and VeriGen by 30.7% and 38.3% in terms of functional correctness under the same base LLM. Notably, SysVCoder achieves performance comparable to NVIDIA's GPT-4 based VerilogCoder while using only a 7B-parameter model, reducing token consumption by 7.6x and synthesis latency by 37.5x. Both SysVCoder and SysVDB are made public at https://gitee.com/sdu-aes-lab/sysvcoder/.