CktFormalizer: Autoformalization of Natural Language into Circuit Representations
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Abstract (arXiv)
Hardware infrastructure is a critical bottleneck for LLM-driven circuit design, limiting what agents can express, compile, and iteratively refine within an agentic loop. To address this bottleneck, we introduce CKTLEAN, a typed hardware infrastructure embedded in Lean. It supports hardware description, compilation to SystemVerilog, and interactive type-checking and proof feedback through a persistent read-eval-print loop (REPL). On this foundation, we build CKTFORMALIZER, an agent framework for hardware generation, repair, optimization, and source-level equivalence proving. We evaluate structural correctness through compilation, functional correctness through RTL and gate-level simulation, and formal correctness through proofs relative to stated specifications. Across VerilogEval, RTLLM, ResBench, and CVDP, CKTFORMALIZER with CKTLEAN achieves compilation rates of 91.1%-99.4%. Among designs that pass RTL simulation, 95.4%-100.0% jointly complete synthesis and place-and-route and pass design-rule and layout-versus-schematic checks. In a separate evaluation on 30 VerilogEval problems, interactive proof-state feedback raises kernel-accepted equivalence proof completion from 53.3% to 63.3%. Hardware evaluation feedback also guides architecture exploration and iterative power, performance, and area (PPA) optimization, with synthesis-area reductions of up to 58.6% in the optimization loop. These results suggest that typed representations and explicit proof-state feedback help structure the agentic loop: compiler diagnostics guide targeted repairs, while proof-state feedback helps agents identify what remains to be proved and determine the next proof step. Project Page: https://ckt-formalizer.github.io/
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References · 17
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- 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 143
- 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 136
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- 2020DAVE: Deriving Automatically Verilog from Englishcited by 46
- 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verificationcited by 41
- 2024CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarizationcited by 38
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