SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning

2025-04-14

AI Summary

To address key challenges in RTL code optimization—including error-prone manual rewriting, limited capability of traditional compilers in handling complex design constraints, and poor alignment between LLM-generated outputs and user intent—this paper proposes the first neuro-symbolic framework. Our method integrates large language model (LLM)-driven RTL rewriting, abstract syntax tree (AST)-based template retrieval-augmented generation (RAG), and fine-grained finite-state machine (FSM) symbolic analysis supporting state merging and partial reduction. It further incorporates formal equivalence checking and test-driven co-verification. This approach transcends the limitations of pattern-matching compilers: on the RTL-Rewriter benchmark, it achieves 43.9% lower power consumption, 62.5% higher performance, and 51.1% smaller area compared to state-of-the-art methods, as validated by Synopsys Design Compiler and Yosys.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Optimizing Register Transfer Level (RTL) code is crucial for improving the power, performance, and area (PPA) of digital circuits in the early stages of synthesis. Manual rewriting, guided by synthesis feedback, can yield high-quality results but is time-consuming and error-prone. Most existing compiler-based approaches have difficulty handling complex design constraints. Large Language Model (LLM)-based methods have emerged as a promising alternative to address these challenges. However, LLM-based approaches often face difficulties in ensuring alignment between the generated code and the provided prompts. This paper presents SymRTLO, a novel neuron-symbolic RTL optimization framework that seamlessly integrates LLM-based code rewriting with symbolic reasoning techniques. Our method incorporates a retrieval-augmented generation (RAG) system of optimization rules and Abstract Syntax Tree (AST)-based templates, enabling LLM-based rewriting that maintains syntactic correctness while minimizing undesired circuit behaviors. A symbolic module is proposed for analyzing and optimizing finite state machine (FSM) logic, allowing fine-grained state merging and partial specification handling beyond the scope of pattern-based compilers. Furthermore, a fast verification pipeline, combining formal equivalence checks with test-driven validation, further reduces the complexity of verification. Experiments on the RTL-Rewriter benchmark with Synopsys Design Compiler and Yosys show that SymRTLO improves power, performance, and area (PPA) by up to 43.9%, 62.5%, and 51.1%, respectively, compared to the state-of-the-art methods.

Problem

• Enhancing RTL code optimization using LLMs and symbolic reasoning
• Addressing alignment issues between generated code and prompts in LLM methods
• Improving power, performance, and area (PPA) in digital circuit synthesis

Innovation

• Integrates LLM-based rewriting with symbolic reasoning
• Uses RAG system for optimization rules and AST templates
• Proposes symbolic module for FSM logic optimization