@article{21554,
  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.},
  author       = {Berggren, Karl and Xia, Qiangfei and Likharev, Konstantin K and Strukov, Dmitri B and Jiang, Hao and Mikolajick, Thomas and Querlioz, Damien and Salinga, Martin and Erickson, John R and Pi, Shuang and Xiong, Feng and Lin, Peng and Li, Can and Chen, Yu and Xiong, Shisheng and Hoskins, Brian D and Daniels, Matthew W and Madhavan, Advait and Liddle, James A and McClelland, Jabez J and Yang, Yuchao and Rupp, Jennifer and Nonnenmann, Stephen S and Cheng, Kwang-Ting and Gong, Nanbo and Lastras-Montaño, Miguel Angel and Talin, A Alec and Salleo, Alberto and Shastri, Bhavin J and de Lima, Thomas Ferreira and Prucnal, Paul and Tait, Alexander N and Shen, Yichen and Meng, Huaiyu and Roques-Carmes, Charles and Cheng, Zengguang and Bhaskaran, Harish and Jariwala, Deep and Wang, Han and Shainline, Jeffrey M and Segall, Kenneth and Yang, J Joshua and Roy, Kaushik and Datta, Suman and Raychowdhury, Arijit},
  issn         = {1361-6528},
  journal      = {Nanotechnology},
  number       = {1},
  publisher    = {IOP Publishing},
  title        = {{Roadmap on emerging hardware and technology for machine learning}},
  doi          = {10.1088/1361-6528/aba70f},
  volume       = {32},
  year         = {2020},
}