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Your search keyword '"Xiong, Shisheng"' showing total 4 results
Start Over Author "Xiong, Shisheng" Journal nanotechnology
4 results on '"Xiong, Shisheng"'

1. Roadmap on emerging hardware and technology for machine learning

Author

Berggren, Karl, primary, Xia, Qiangfei, additional, Likharev, Konstantin K, additional, Strukov, Dmitri B, additional, Jiang, Hao, additional, Mikolajick, Thomas, additional, Querlioz, Damien, additional, Salinga, Martin, additional, Erickson, John R, additional, Pi, Shuang, additional, Xiong, Feng, additional, Lin, Peng, additional, Li, Can, additional, Chen, Yu, additional, Xiong, Shisheng, additional, Hoskins, Brian D, additional, Daniels, Matthew W, additional, Madhavan, Advait, additional, Liddle, James A, additional, McClelland, Jabez J, additional, Yang, Yuchao, additional, Rupp, Jennifer, additional, Nonnenmann, Stephen S, additional, Cheng, Kwang-Ting, additional, Gong, Nanbo, additional, Lastras-Montaño, Miguel Angel, additional, Talin, A Alec, additional, Salleo, Alberto, additional, Shastri, Bhavin J, additional, de Lima, Thomas Ferreira, additional, Prucnal, Paul, additional, Tait, Alexander N, additional, Shen, Yichen, additional, Meng, Huaiyu, additional, Roques-Carmes, Charles, additional, Cheng, Zengguang, additional, Bhaskaran, Harish, additional, Jariwala, Deep, additional, Wang, Han, additional, Shainline, Jeffrey M, additional, Segall, Kenneth, additional, Yang, J Joshua, additional, Roy, Kaushik, additional, Datta, Suman, additional, and Raychowdhury, Arijit, additional

Published
2020
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3. Roadmap on emerging hardware and technology for machine learning.

Author

Berggren, Karl, Xia, Qiangfei, Likharev, Konstantin K, Strukov, Dmitri B, Jiang, Hao, Mikolajick, Thomas, Querlioz, Damien, Salinga, Martin, Erickson, John R, Pi, Shuang, Xiong, Feng, Lin, Peng, Li, Can, Chen, Yu, Xiong, Shisheng, Hoskins, Brian D, Daniels, Matthew W, Madhavan, Advait, Liddle, James A, and McClelland, Jabez J

Subjects
MACHINE learning, ARTIFICIAL neural networks, TECHNOLOGICAL innovations, ALGORITHMS, ARTIFICIAL intelligence
Abstract

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field. [ABSTRACT FROM AUTHOR]

Published
2021
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