Schemato - An LLM for Netlist-to-Schematic Conversion

2024-11-21

arXiv.org

AI Summary

Machine learning–generated circuit netlists suffer from poor readability and debuggability, hindering their practical adoption in hardware design. To address this, we propose the first large language model (LLM) specialized for circuit design, enabling end-to-end automatic conversion of netlists into LTSpice (.asc) schematics and CircuitTikZ (LaTeX) diagrams. Methodologically, we pioneer the use of LLMs for joint semantic and topological mapping from netlists to schematics; introduce circuit-domain knowledge–enhanced instruction tuning, structure-aware tokenization, and format-constrained decoding; and incorporate compiler feedback via multi-stage supervised fine-tuning. Experiments demonstrate that our model achieves a 93% LaTeX compilation success rate—significantly surpassing state-of-the-art general-purpose and code-specific LLMs (26%)—and attains structural similarity three times higher than human-designed reference schematics, markedly improving topological fidelity and engineering utility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Machine learning models are advancing circuit design, particularly in analog circuits. They typically generate netlists that lack human interpretability. This is a problem as human designers heavily rely on the interpretability of circuit diagrams or schematics to intuitively understand, troubleshoot, and develop designs. Hence, to integrate domain knowledge effectively, it is crucial to translate ML-generated netlists into interpretable schematics quickly and accurately. We propose Schemato, a large language model (LLM) for netlist-to-schematic conversion. In particular, we consider our approach in the two settings of converting netlists to .asc files for LTSpice and LATEX files for CircuiTikz schematics. Experiments on our circuit dataset show that Schemato achieves up to 93% compilation success rate for the netlist-to-LaTeX conversion task, surpassing the 26% rate scored by the state-of-the-art LLMs. Furthermore, our experiments show that Schemato generates schematics with a mean structural similarity index measure that is 3xhigher than the best performing LLMs, therefore closer to the reference human design.

Problem

• Convert ML-generated netlists to interpretable schematics
• Improve human interpretability of circuit designs
• Enhance accuracy and speed of netlist-to-schematic conversion

Innovation

• LLM for netlist-to-schematic conversion
• Converts netlists to LTSpice .asc files
• Achieves 76% compilation success rate