Zobrazeno 1 - 10
of 960
pro vyhledávání: '"Che, Kai"'
Autor:
Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Raj, Ritik, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Li, Sixu, Kim, Youbin, Samajdar, Ananda, Lin, Yingyan Celine, Ibrahim, Mohamed, Rabaey, Jan M., Krishna, Tushar, Raychowdhury, Arijit
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-gener
Externí odkaz:
http://arxiv.org/abs/2409.13153
Autor:
Wan, Zishen, Liu, Che-Kai, Ibrahim, Mohamed, Yang, Hanchen, Spetalnick, Samuel, Krishna, Tushar, Raychowdhury, Arijit
Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An elegant approach to represent this intricate factorization is via h
Externí odkaz:
http://arxiv.org/abs/2404.04173
Autor:
Xu, Zhicheng, Liu, Che-Kai, Li, Chao, Mao, Ruibin, Yang, Jianyi, Kämpfe, Thomas, Imani, Mohsen, Li, Can, Zhuo, Cheng, Yin, Xunzhao
Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effectively, exacerbating the data-transfer bottleneck inherent in the conv
Externí odkaz:
http://arxiv.org/abs/2401.05708
Autor:
Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Krishna, Tushar, Lin, Yingyan, Raychowdhury, Arijit
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the current challenges surrounding unsustainable computational trajectories, lim
Externí odkaz:
http://arxiv.org/abs/2401.01040
Autor:
Fayza, Farbin, Demirkiran, Cansu, Chen, Hanning, Liu, Che-Kai, Mohan, Avi, Errahmouni, Hamza, Yun, Sanggeon, Imani, Mohsen, Zhang, David, Bunandar, Darius, Joshi, Ajay
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs make it ext
Externí odkaz:
http://arxiv.org/abs/2311.17801
Autor:
Shou, Shengxi, Liu, Che-Kai, Yun, Sanggeon, Wan, Zishen, Ni, Kai, Imani, Mohsen, Hu, X. Sharon, Yang, Jianyi, Zhuo, Cheng, Yin, Xunzhao
In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM designs e
Externí odkaz:
http://arxiv.org/abs/2310.04940
Autor:
Chu-Chun Cheng, Ruei-Fong Tsai, Che-Kai Lin, Kui-Thong Tan, Vidmantas Kalendra, Mantas Simenas, Chun-Wei Lin, Yun-Wei Chiang
Publikováno v:
JACS Au, Vol 4, Iss 10, Pp 3766-3770 (2024)
Externí odkaz:
https://doaj.org/article/ddcf4b67e7774cac93e7d6be8502c27b
Autor:
Liu, Che-Kai, Chen, Haobang, Imani, Mohsen, Ni, Kai, Kazemi, Arman, Laguna, Ann Franchesca, Niemier, Michael, Hu, Xiaobo Sharon, Zhao, Liang, Zhuo, Cheng, Yin, Xunzhao
In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, performing the cosine similarities between the vectors in Von-Neumann m
Externí odkaz:
http://arxiv.org/abs/2207.12188
Autor:
Chakrani, Zakaria, Patel, Mann, Mellgard, George, McCroskery, Stephen, Saffran, Nathaniel, Taylor, Nicole, Liaw, Bobby C., Galsky, Matthew, Oh, William, Tsao, Che-Kai, Ganta, Teja, Patel, Vaibhav
Publikováno v:
In Clinical Genitourinary Cancer December 2024 22(6)
Publikováno v:
In Clinical Genitourinary Cancer June 2024 22(3)