arXiv Machine Learning By Senol Gulgonul

Weave: Verified Netlist-to-Schematic Conversion via Layered Graph Layout

Read the original on arXiv Machine Learning →

arXiv:2607. 03835v1 Announce Type: cross Abstract: Converting a SPICE netlist into a human-readable schematic is a longstanding problem in electronic design automation: simulators and machine-learning pipelines readily produce netlists, but designers reason about circuits through diagrams.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
6d ago

NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation

arXiv:2608. 12197v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in circuit design workflows, yet their reliability on simulator-facing SPICE netlist recognition and manipulation remains poorly understood and is rarely separated from high-level design reasoning.

By Jiarui Ma, Jianghan Wang, Yuheng Ma, Ziyi Zhuang, Xiaoguang Liu
arXiv Machine Learning
Jul 3

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

arXiv:2607. 01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks.

By Saoud Aldowaish, Yashwanth Karumanchi, Kai-Chen Chiang, Mohammed Ayman Habib, Finn Murphy, Rishen Cao, Morteza Fayazi
Hugging Face Trending Papers
Jul 2

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs) for circuit design tasks. However, their application to analog and mixed-signal domains remains limited by the lack of machine-readable representations of existing circuit design knowledge.