LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation

TL;DR

This paper introduces LLM-FSM, a new benchmark for evaluating large language models' ability to understand and generate finite-state machine behavior from natural language, with a focus on hardware RTL code generation.

Contribution

The paper presents LLM-FSM, an automated, scalable benchmark for finite-state reasoning in RTL code generation, and analyzes LLM performance with scaling and fine-tuning.

Findings

LLMs' accuracy declines with increased FSM complexity.

Supervised fine-tuning improves out-of-distribution generalization.

Test-time compute scaling enhances reasoning reliability.

Abstract

Finite-state reasoning, the ability to understand and implement state-dependent behavior, is central to hardware design. In this paper, we present LLM-FSM, a benchmark that evaluates how well large language models (LLMs) can recover finite-state machine (FSM) behavior from natural-language specifications and translate it into correct register transfer-level (RTL) implementations. Unlike prior specification-to-RTL benchmarks that rely on manually constructed examples, LLM-FSM is built through a fully automated pipeline. LLM-FSM first constructs FSM with configurable state counts and constrained transition structures. It then prompts LLMs to express each FSM in a structured YAML format with an application context, and to further convert that YAML into a natural-language (NL) specification. From the same YAML, our pipeline synthesizes the reference RTL and testbench in a correct-by-construction manner. All 1,000 problems are verified using LLM-based and SAT-solver-based checks, with human review on a subset. Our experiments show that even the strongest LLMs exhibit sharply declining accuracy as FSM complexity increases. We further demonstrate that training-time scaling via supervised fine-tuning (SFT) generalizes effectively to out-of-distribution (OOD) tasks, while increasing test-time compute improves reasoning reliability. Finally, LLM-FSM remains extensible by allowing its FSM complexity to scale with future model capabilities.