VeriOpt: PPA-Aware High-Quality Verilog Generation via Multi-Role LLMs

2025-07-19

AI Summary

Current large language models (LLMs) for hardware design primarily focus on Verilog functional correctness, neglecting industrial-grade PPA (Power-Performance-Area) constraints. This work proposes a PPA-aware, multi-role collaborative code generation framework: it explicitly models the hardware design workflow via role-specific prompting—planning, programming, reviewing, and evaluating—and integrates PPA constraints directly into the prompting mechanism. Leveraging multimodal feedback—including synthesized EDA reports and timing diagrams—generated by commercial EDA tools, the framework enables closed-loop optimization. Experimental results demonstrate that, compared to baseline methods, our approach achieves up to 88% power reduction, 76% area reduction, and a 73% improvement in timing closure rate, while maintaining 86% functional correctness. These gains significantly enhance the synthesizability and industrial deployability of the generated Verilog code.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The rapid adoption of large language models(LLMs) in hardware design has primarily focused on generating functionally correct Verilog code, overlooking critical Power Performance-Area(PPA) metrics essential for industrial-grade designs. To bridge this gap, we propose VeriOpt, a novel framework that leverages role-based prompting and PPA-aware optimization to enable LLMs to produce high-quality, synthesizable Verilog. VeriOpt structures LLM interactions into specialized roles (e.g., Planner, Programmer, Reviewer, Evaluator) to emulate human design workflows, while integrating PPA constraints directly into the prompting pipeline. By combining multi-modal feedback (e.g., synthesis reports, timing diagrams) with PPA aware prompting, VeriOpt achieves PPA-efficient code generation without sacrificing functional correctness. Experimental results demonstrate up to 88% reduction in power, 76% reduction in area and 73% improvement in timing closure compared to baseline LLM-generated RTL, validated using industry standard EDA tools. At the same time achieves 86% success rate in functionality evaluation. Our work advances the state-of-the-art AI-driven hardware design by addressing the critical gap between correctness and quality, paving the way for reliable LLM adoption in production workflows.

Problem

• Generating Verilog code meeting industrial PPA metrics
• Bridging gap between functional correctness and design quality
• Optimizing power, area, timing in LLM-generated hardware designs

Innovation

• Uses role-based prompting for Verilog generation
• Integrates PPA constraints into prompting pipeline
• Combines multi-modal feedback with PPA optimization