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.

Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training

1 code implementation • 3 Jun 2023 • Pucheng Dang, Xing Hu, Kaidi Xu, Jinhao Duan, Di Huang, Husheng Han, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen

Unlearning techniques are proposed to prevent third parties from exploiting unauthorized data, which generate unlearnable samples by adding imperceptible perturbations to data for public publishing.

Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

no code implementations • 19 Aug 2022 • Husheng Han, Xing Hu, Kaidi Xu, Pucheng Dang, Ying Wang, Yongwei Zhao, Zidong Du, Qi Guo, Yanzhi Yang, Tianshi Chen

This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust video object detection.

Adversarial Attack Autonomous Driving +4

ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

no code implementations • NeurIPS 2021 • Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling Liang, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen

Our experimental results show that the certified accuracy is increased from 36. 3% (the state-of-the-art certified detection) to 60. 4% on the ImageNet dataset, largely pushing the certified defenses for practical use.