@article{Guan2026, 
author = {Xiang-Tao Guan and Shu-Yao Cheng and Mo Zou and Rui Zhang and Yun-Ji Chen},
title = {Data-Driven Automated Processor Design},
year = {2026},
journal = {Journal of Computer Science and Technology},
volume = {41},
number = {1},
pages = {103-113},
keywords = {automated processor design, data-driven design, binary speculative diagram},
url = {https://www.sciopen.com/article/10.1007/s11390-026-6043-x},
doi = {10.1007/s11390-026-6043-x},
abstract = {Fully automated processor design has recently gained significant popularity due to its fast convergence speed and reduced human costs. However, automated design remains challenging in processor correctness and performance guarantee. In this article, we introduce a series of processor auto-design methods based on a data-driven method, Binary Speculative Diagram (BSD), emphasizing how they guarantee design correctness and improve the auto-designed processor performance. Auto-designed by BSD, QiMeng-CPU-v1, an industrial-scale RISC-V CPU, achieves up to 99.99999999999% accuracy. Auto-designed by State-BSD, QiMeng-CPU-v2 is comparable to ARM Cortex A53 (2010s CPU), a human-designed superscalar processor. Finally, we discuss potential future directions for extending and improving the proposed design methods toward more generalized automated processor architectures.}
}