Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Syuan-Hao Sie"'
Autor:
Wei-Chen Wei, Chuan-Jia Jhang, Yi-Ren Chen, Cheng-Xin Xue, Syuan-Hao Sie, Jye-Luen Lee, Hao-Wen Kuo, Chih-Cheng Lu, Meng-Fan Chang, Kea-Tiong Tang
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 45-52 (2020)
Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to
Externí odkaz:
https://doaj.org/article/150e34966e584bc78db48c13b4a4632c
MARS: Multimacro Architecture SRAM CIM-Based Accelerator With Co-Designed Compressed Neural Networks
Autor:
Jye-Luen Lee, Chih-Cheng Hsieh, Zuo-Wei Yeh, Kea-Tiong Tang, Syuan-Hao Sie, Meng-Fan Chang, Yi-Ren Chen, Zhaofang Li, Chih-Cheng Lu
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41:1550-1562
Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computation cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has
Autor:
Chih-Cheng Lu, Meng-Fan Chang, Cheng-Xin Xue, Wei-Chen Wei, Jye-Luen Lee, Hao-Wen Kuo, Syuan-Hao Sie, Kea-Tiong Tang, Chuan-Jia Jhang, Yi-Ren Chen
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 45-52 (2020)
Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to
Autor:
Zhaofang Li, Syuan-Hao Sie, Jye-Luen Lee, Yi-Ren Chen, Ting-I Chou, Ping-Chun Wu, Yu-Ting Chuang, Yu-Te Lin, I-Cherng Chen, Chih-Cheng Lu, Ying-Zong Juang, Shih-Wen Chiu, Chih-Cheng Hsieh, Meng-Fan Chang, Kea-Tiong Tang
Publikováno v:
2021 IEEE International Electron Devices Meeting (IEDM).
Autor:
Yun-Chen Lo, Yen-Chi Chou, Meng-Fan Chang, Qiang Li, Kea-Tiong Tang, Ruhui Liu, Wei-Chen Wei, Tzu-Hsiang Hsu, Yen-Kai Chen, Ssu-Yen Wu, Zhixiao Zhang, Xin Si, Wei-Chiang Shih, Yajuan He, Chung-Chuan Lo, Syuan-Hao Sie, Jing-Hong Wang, Chih-Cheng Hsieh, Ta-Wei Liu, Yung-Ning Tu, William Shih, Ren-Shuo Liu, Nan-Chun Lien, Jian-Wei Su, Wei-Hsing Huanq, Pei-Jung Lu, Tai-Hsing Wen
Publikováno v:
ISSCC
Advanced AI edge chips require multibit input (IN), weight (W), and output (OUT) for CNN multiply-and-accumulate (MAC) operations to achieve an inference accuracy that is sufficient for practical applications. Computing-in-memory (CIM) is an attracti
Autor:
Yi-Ren Chen, Wen-Chien Ting, Meng-Fan Chang, Cheng-Te Wang, Jun-Shen Wu, Kea-Tiong Tang, Ren-Shuo Liu, Yen-Kai Chen, Chung-Chuan Lo, Syuan-Hao Sie, Chih-Cheng Hsieh, Tzu-Hsiang Hsu, Chen-Fu Yeh
Publikováno v:
ISSCC
Energy-efficient always-on motion-detection (MD) sensors are in high demand and are widely used in machine vision applications. To achieve real-time and continuous motion monitoring, high-speed low-power temporal difference imagers with corresponding