Trinity: A General Purpose FHE Accelerator

Autor: Deng, Xianglong, Fan, Shengyu, Hu, Zhicheng, Tian, Zhuoyu, Yang, Zihao, Yu, Jiangrui, Cao, Dingyuan, Meng, Dan, Hou, Rui, Li, Meng, Lou, Qian, Zhang, Mingzhe

In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single accelerator. To achieve this goal, we first analyze the theoretical fo

Externí odkaz: http://arxiv.org/abs/2410.13405

Communication-Free Robust Wireless Power Transfer with Constant Output Power and Stable Frequency

Autor: Zhang, Zhuoyu, Lai, Junan, Huang, Yuangen, Hao, Xianglin, Yin, Ke, Jiang, Zhiqin, Wang, Chao, Ma, Xikui, Huang, Ming, Dong, Tianyu

A primary challenge in wireless power transfer (WPT) systems is to achieve efficient and stable power transmission without complex control strategies when load conditions change dynamically. Addressing this issue, we propose a third-order pseudo-Herm

Externí odkaz: http://arxiv.org/abs/2408.15818

Gigantic-oxidative atomic-layer-by-layer epitaxy for artificially designed complex oxides

Autor: Zhou, Guangdi, Huang, Haoliang, Wang, Fengzhe, Wang, Heng, Yang, Qishuo, Nie, Zihao, Lv, Wei, Ding, Cui, Li, Yueying, Lin, Jiayi, Yue, Changming, Li, Danfeng, Sun, Yujie, Lin, Junhao, Zhang, Guang-Ming, Xue, Qi-Kun, Chen, Zhuoyu

In designing material functionalities for transition metal oxides, lattice structure and d-orbital occupancy are key determinants. However, the modulation of these two factors is inherently limited by the need to balance thermodynamic stability, grow

Externí odkaz: http://arxiv.org/abs/2406.16520

Technique Report of CVPR 2024 PBDL Challenges

Autor: Fu, Ying, Li, Yu, You, Shaodi, Shi, Boxin, Chen, Linwei, Zou, Yunhao, Wang, Zichun, Li, Yichen, Han, Yuze, Zhang, Yingkai, Wang, Jianan, Liu, Qinglin, Yu, Wei, Lv, Xiaoqian, Li, Jianing, Zhang, Shengping, Ji, Xiangyang, Chen, Yuanpei, Zhang, Yuhan, Peng, Weihang, Zhang, Liwen, Xu, Zhe, Gou, Dingyong, Li, Cong, Xu, Senyan, Zhang, Yunkang, Jiang, Siyuan, Lu, Xiaoqiang, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Xie, Haiyang, Zhao, Jian, Huang, Shihua, Cheng, Peng, Shen, Xi, Wang, Zheng, An, Shuai, Zhu, Caizhi, Li, Xuelong, Zhang, Tao, Li, Liang, Liu, Yu, Yan, Chenggang, Zhang, Gengchen, Jiang, Linyan, Song, Bingyi, An, Zhuoyu, Lei, Haibo, Luo, Qing, Song, Jie, Liu, Yuan, Li, Qihang, Zhang, Haoyuan, Wang, Lingfeng, Chen, Wei, Luo, Aling, Li, Cheng, Cao, Jun, Chen, Shu, Dou, Zifei, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Gou, Xuejian, Wang, Qinliang, Liu, Yang, Zhao, Shizhan, Zhang, Yanzhao, Yan, Libo, Guo, Yuwei, Li, Guoxin, Gao, Qiong, Che, Chenyue, Sun, Long, Chen, Xiang, Li, Hao, Pan, Jinshan, Xie, Chuanlong, Chen, Hongming, Li, Mingrui, Deng, Tianchen, Huang, Jingwei, Li, Yufeng, Wan, Fei, Xu, Bingxin, Cheng, Jian, Liu, Hongzhe, Xu, Cheng, Zou, Yuxiang, Pan, Weiguo, Dai, Songyin, Jia, Sen, Zhang, Junpei, Chen, Puhua

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and ac

Externí odkaz: http://arxiv.org/abs/2406.10744

Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer

Autor: You, Junwei, Chen, Ying, Jiang, Zhuoyu, Liu, Zhangchi, Huang, Zilin, Ding, Yifeng, Ran, Bin

Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates. This paper presents the Gramian Angular Field Vi

Externí odkaz: http://arxiv.org/abs/2310.13906