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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Vydané v:	Nanotechnology Ročník 32; číslo 1; s. 012002 - 12046
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England IOP Publishing 01.01.2021
Predmet:	artificial intelligence hardware technologies machine learning neural network models neuromorphic computing
ISSN:	0957-4484, 1361-6528, 1361-6528
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