Nanotechnology, volume 32, issue 1, pages 12002

Roadmap on emerging hardware and technology for machine learning

Karl K Berggren ¹

Qiangfei Xia ²

Konstantin K. Likharev ³

Dmitri Strukov ⁴

Hao Jiang ⁵

Thomas Mikolajick ⁶

Damien Querlioz ⁷

Martin Salinga ⁸

John R Erickson ⁹

Shuang Pi ¹⁰

Feng Xiong ⁹

Peng Lin ¹

C X Li ¹¹

Yu Chen ¹²

Shisheng Xiong ¹²

Brian D. Hoskins ¹³

Matthew W. Daniels ¹³

Advait Madhavan ^{13, 14}

James A Liddle ¹³

Jabez J. McClelland ¹³

Yuchao Yang ¹⁵

Jennifer L. Rupp ^{16, 17}

Stephen S. Nonnenmann ¹⁸

Kwang-Ting Cheng ¹⁹

N Gong ²⁰

M A Lastras Montaño ²¹

Alec Talin ²²

Alberto Salleo ²³

Bhavin J. Shastri ²⁴

Thomas Ferreira de Lima ²⁵

Paul Prucnal ²⁵

Alexander N. Tait ²⁶

Yichen Shen ²⁷

Huaiyu Meng ²⁷

Charles Roques-Carmes ¹

Z. Cheng ^{28, 29}

H. Bhaskaran ³⁰

Deep Jariwala ³¹

Hao-Zhe Wang ³²

Jeffrey M. Shainline ²⁶

Kenneth Segall ³³

Jyisy Yang ²

Kaushik Roy ³⁴

S. Datta ³⁵

A. Raychowdhury ³⁶

Show full list: 45 authors

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Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America. |

Department of Electrical and Computer Engineering, University of Massachusetts, Amherst MA, United States of America. |

Stony Brook University, Stony Brook, NY 11794, Unites States |

⁴

Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, United States of America |

⁵

School of Engineering & Applied Science Yale University, CT, United States of America |

⁶

NaMLab gGmbH and TU Dresden, Germany |

⁷

Université Paris-Saclay, CNRS, France |

⁸

Institut für Materialphysik, Westfälische Wilhelms-Universität Münster, Germany |

⁹

Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States of America |

¹⁰

Lam Research, Fremont, CA, United States of America |

¹¹

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR (China) |

¹²

School of Information Science & Technology, Fudan University, Shanghai, People’s Republic of China |

¹³

Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, United States of America |

¹⁴

Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, United States of America |

¹⁵

School of Electronics Engineering and Computer Science, Peking University,Beijing,People’s Republic of China |

¹⁶

Department of Materials Science and Engineering and Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America |

¹⁷

Electrochemical Materials, ETHZ Department of Materials, Hönggerbergring 64, Zürich 8093, Switzerland |

¹⁸

Department of Mechanical & Industrial Engineering, University of Massachusetts-Amherst, MA, United States of America |

¹⁹

School of Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, People’s Republic of China |

²⁰

IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, United States of America |

²¹

Instituto de Investigación en Comunicación Óptica, Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, México |

²²

Sandia National Laboratories, Livermore, CA 94551, United States of America |

²³

Department of Materials Science and Engineering, Stanford University, Stanford, California, United States of America |

²⁴

Department of Physics, Engineering Physics & Astronomy, Queen’s University, Kingston, ON KL7 3N6, Canada |

²⁵

Department of Electrical Engineering, Princeton University, Princeton NJ 08544, United States of America |

²⁶

Physical Measurement Laboratory, National Institute of Standards and Technology (NIST), Boulder, CO 80305, United States of America |

²⁷

Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America |

²⁸

Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom |

²⁹

State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, People’s Republic of China |

³⁰

Department of Materials University of Oxford Oxford OX1 3PH United Kingdom |

³¹

Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, United States of America |

³²

University of Southern California, Los Angeles, CA 90089, United States of America |

³³

Department of Physics and Astronomy, Colgate University, NY 13346, United States of America |

³⁴

School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States of America |

³⁵

University of Notre Dame, Notre Dame, IN, 46556, United States of America. |

³⁶

Georgia Institute of Technology, Atlanta, GA 30332 United States of America |

Publication type: Journal Article

Publication date: 2020-10-20

IOP Publishing

Journal: Nanotechnology

scimago Q2

SJR: 0.631

CiteScore: 7.1

Impact factor: 2.9

ISSN: 09574484, 13616528

DOI: 10.1088/1361-6528/aba70f

Copy DOI

PubMed ID: 32679577

General Chemistry

General Materials Science

Electrical and Electronic Engineering

Mechanical Engineering

Bioengineering

Mechanics of Materials

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.

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