Pushing the Limits of Machine Design: Automated CPU Design with AI

1 code implementation • 21 Jun 2023 • Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen

By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours.

ANPL: Towards Natural Programming with Interactive Decomposition

1 code implementation • NeurIPS 2023 • Di Huang, Ziyuan Nan, Xing Hu, Pengwei Jin, Shaohui Peng, Yuanbo Wen, Rui Zhang, Zidong Du, Qi Guo, Yewen Pu, Yunji Chen

We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together.

Code Generation Program Synthesis

Online Symbolic Regression with Informative Query

no code implementations • 21 Feb 2023 • Pengwei Jin, Di Huang, Rui Zhang, Xing Hu, Ziyuan Nan, Zidong Du, Qi Guo, Yunji Chen

Symbolic regression, the task of extracting mathematical expressions from the observed data $\{ \vx_i, y_i \}$, plays a crucial role in scientific discovery.

regression Symbolic Regression

Neural Program Synthesis with Query

no code implementations • ICLR 2022 • Di Huang, Rui Zhang, Xing Hu, Xishan Zhang, Pengwei Jin, Nan Li, Zidong Du, Qi Guo, Yunji Chen

In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.

Program Synthesis