QiMeng-ChipV-RTL is a multi‑agent framework that tackles the challenges of generating Register‑Transfer Level (RTL) code for industrial IP‑level hardware design. By partitioning long design documents into short, localized tasks and using hierarchical planning, localized code generation, interface‑consistent merging, and AST‑guided debugging, it scales to complex specifications. Experiments on the RealBench benchmark show ChipV-RTL achieves a 45.0% pass rate, outperforming state‑of‑the‑art LLMs and agents which reach only 21.6%.
By Hanqi Lyu, Di Huang, Yaoyu Zhu, Kangcheng Liu, Bohan Dou, Chongxiao Li, Pengwei Jin, Shuyao Cheng, Rui Zhang, Zidong Du, Qi Guo, Xing Hu, Yunji Chen
Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, production-ready mechanical assembly generation remains largely unsolved.
arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.
By Yurui Dong, Shu Zou, Siqi Li, Nianchen Deng, Hongbin Zhou, Xuemeng Yang, Pinlong Cai, Licheng Wen, Xinyu Cai, Botian Shi
arXiv:2606. 17074v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) is increasingly used for applications in the hardware and software domains.
By Sahana Srinivasan, Benjamin Turnbull, Hammond Pearce
PCBnet is a new large‑scale dataset of printed circuit board (PCB) schematics that includes over 300 real‑world designs, more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters, each paired with a SPICE netlist. The authors also introduce an automated pipeline that converts schematic images into netlists, achieving 94.54% component detection mAP, 98.57% text recognition accuracy, and 84.47% end‑to‑end connectivity accuracy. This resource aims to serve as a benchmark and data foundation for future AI‑driven PCB design automation.
By Zhen Huang, Yuhao Gao, Yuzhi Liu, Daian Cheng, Chengyuan Shao, Yucheng Chen, Yongjian Jia, Futing Zhang, Yichen Shi, Wenhao Wang, Zuyan He, Yangbo Wei, Zhanfei Chen, Jinlong Yan, Yu Zhang, Haoying Wu, Ting-Jung Lin, Lei He
arXiv:2607. 09616v1 Announce Type: cross Abstract: As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development.
By Kangwei Xu, Bing Li, Ulf Schlichtmann