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. © 2020 IOP Publishing Ltd.

authors

• Berggren K.
• Bhaskaran H.
• Chen Y.
• Cheng K.-T.
• Cheng Z.
• Daniels M.W.
• Datta S.
• De Lima T.F.
• Erickson J.R.
• Gong N.
• Hoskins B.
• Jariwala D.
• Jiang H.
• Lastras Montaño Miguel Angel
• Li C.
• Liddle J.A.
• Likharev K.K.
• Lin P.
• Madhavan A.
• McClelland J.J.
• Meng H.
• Mikolajick T.
• Nonnenmann S.S.
• Pi S.
• Prucnal P.
• Querlioz D.
• Raychowdhury A.
• Roques-Carmes C.
• Roy K.
• Rupp J.
• Salinga M.
• Salleo A.
• Segall K.
• Shainline J.M.
• Shastri B.J.
• Shen Y.
• Strukov D.B.
• Tait A.N.
• Talin A.A.
• Wang H.
• Xia Q.
• Xiong F.
• Xiong S.
• Yang J.J.
• Yang Y.

publication date

• 2020-01-01

published in

• Nanotechnology  Journal

keywords

• Artificial intelligence; Hardware technologies; Machine learning; Neural network models; Neuromorphic computing Computer architecture; Computer hardware; Digital computers; Energy efficiency; Green computing; Network architecture; Circuit fabrication; Device optimization; Hardware technology; Materials selection; Neumann architecture; Neural network model; Neuromorphic computing; System integration; Machine learning; article; artificial intelligence; artificial neural network; human; nanotechnology

Digital Object Identifier (DOI)

• 10.1088/1361-6528/aba70f

PubMed ID

• 32679577

start page

end page

volume

• 32

issue

• 1