Roadmap on emerging hardware and technology for machine learning

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 li...

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Veröffentlicht in:	Nanotechnology Jg. 32; H. 1; S. 012002 - 12046
Hauptverfasser:	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, McClelland, Jabez J, Yang, Yuchao, Rupp, Jennifer, Nonnenmann, Stephen S, Cheng, Kwang-Ting, Gong, Nanbo, Lastras-Montaño, Miguel Angel, Talin, A Alec, Salleo, Alberto, Shastri, Bhavin J, de Lima, Thomas Ferreira, Prucnal, Paul, Tait, Alexander N, Shen, Yichen, Meng, Huaiyu, Roques-Carmes, Charles, Cheng, Zengguang, Bhaskaran, Harish, Jariwala, Deep, Wang, Han, Shainline, Jeffrey M, Segall, Kenneth, Yang, J Joshua, Roy, Kaushik, Datta, Suman, Raychowdhury, Arijit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England IOP Publishing 01.01.2021
Schlagworte:	artificial intelligence hardware technologies machine learning neural network models neuromorphic computing
ISSN:	0957-4484, 1361-6528, 1361-6528
Online-Zugang:	Volltext
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