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...

Celý popis

Podrobná bibliografie

Vydáno v:	Nanotechnology Ročník 32; číslo 1; s. 012002 - 12046
Hlavní autoři:	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:	angličtina
Vydáno:	England IOP Publishing 01.01.2021
Témata:	artificial intelligence hardware technologies machine learning neural network models neuromorphic computing
ISSN:	0957-4484, 1361-6528, 1361-6528
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!