Title: LocalV: Exploiting Information Locality for IP-level Verilog Generation
Primary area: generative models
Keywords: Large Language Model, Verilog Generation, Multi Agent Framework

Abstract: The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engineers to manually translate complex specifications into thousands of lines of synthesizable Hardware Description Language (HDL) code. While Large Language Models (LLMs) have shown promise in automating this process, existing approaches—including fine-tuned domain-specific models and advanced agent-based systems—struggle to scale to industrial IP-level design tasks. We identify three key challenges: (1) handling long, highly detailed documents, where critical interface constraints become buried in unrelated submodule descriptions; (2) generating long RTL code, where both syntactic and semantic correctness degrade sharply with increasing output length; and (3) navigating the complex debugging cycles required for functional verification through simulation and waveform analysis.
To overcome these challenges, we propose \textit{LocalV}, a multi-agent framework that leverages the inherent \textit{information locality} in modular hardware design. LocalV decomposes the long-document to long-code generation problem into a set of short-document, short-code tasks, enabling scalable generation and debugging. Specifically, LocalV integrates hierarchical document partitioning, task planning, localized code generation, interface-consistent merging, and AST-guided locality-aware debugging. Experiments on \textsc{RealBench} demonstrate that LocalV substantially outperforms state-of-the-art (SOTA) LLMs and agents, showing the potential of generating Verilog for IP-level RTL design.