Roadmap on emerging hardware and technology for machine learning

Karl Berggren, Qiangfei Xia, Konstantin K. Likharev, Dmitri B. Strukov, Hao Jiang, Thomas Mikolajick, Damien Querlioz, Martin Salinga, John R. Erickson, Shuang Pi, Feng Xiong, Peng Lin, Can Li, Yu Chen, Shisheng Xiong, Brian D. Hoskins, Matthew W. Daniels, Advait Madhavan, James A. Liddle, Jabez J. McClellandYuchao Yang, Jennifer Rupp, Stephen S. Nonnenmann, Kwang Ting Cheng, Nanbo Gong, Miguel Angel Lastras-Montaño, A. Alec Talin, Alberto Salleo, Bhavin J. Shastri, Thomas Ferreira De Lima, Paul Prucnal, Alexander N. Tait, Yichen Shen, Huaiyu Meng, Charles Roques-Carmes, Zengguang Cheng, Harish Bhaskaran, Deep Jariwala, Han Wang, Jeffrey M. Shainline, Kenneth Segall, J. Joshua Yang, Kaushik Roy, Suman Datta, Arijit Raychowdhury

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Article number012002
JournalNanotechnology
Volume32
Issue number1
DOIs
StatePublished - Oct 19 2020

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Keywords

  • Artificial intelligence
  • Hardware technologies
  • Machine learning
  • Neural network models
  • Neuromorphic computing

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