article published yes Karl Berggren author Qiangfei Xia author Konstantin K Likharev author Dmitri B Strukov author Hao Jiang author Thomas Mikolajick author Damien Querlioz author Martin Salinga author John R Erickson author Shuang Pi author Feng Xiong author Peng Lin author Can Li author Yu Chen author Shisheng Xiong author Brian D Hoskins author Matthew W Daniels author Advait Madhavan author James A Liddle author Jabez J McClelland author Yuchao Yang author Jennifer Rupp author Stephen S Nonnenmann author Kwang-Ting Cheng author Nanbo Gong author Miguel Angel Lastras-Montaño author A Alec Talin author Alberto Salleo author Bhavin J Shastri author Thomas Ferreira de Lima author Paul Prucnal author Alexander N Tait author Yichen Shen author Huaiyu Meng author Charles Roques-Carmes author e2e68fc9-6505-11ef-a541-eb4e72cc3e82 Zengguang Cheng author Harish Bhaskaran author Deep Jariwala author Han Wang author Jeffrey M Shainline author Kenneth Segall author J Joshua Yang author Kaushik Roy author Suman Datta author Arijit Raychowdhury author 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. https://creativecommons.org/licenses/by/4.0/ IOP Publishing2020 eng 0957-4484 1361-6528 3267957710.1088/1361-6528/aba70f 321 yes K. Berggren <i>et al.</i>, “Roadmap on emerging hardware and technology for machine learning,” <i>Nanotechnology</i>, vol. 32, no. 1. IOP Publishing, 2020. Berggren K, Xia Q, Likharev KK, et al. Roadmap on emerging hardware and technology for machine learning. <i>Nanotechnology</i>. 2020;32(1). doi:<a href="https://doi.org/10.1088/1361-6528/aba70f">10.1088/1361-6528/aba70f</a> K. Berggren, Q. Xia, K.K. Likharev, D.B. Strukov, H. Jiang, T. Mikolajick, D. Querlioz, M. Salinga, J.R. Erickson, S. Pi, F. Xiong, P. Lin, C. Li, Y. Chen, S. Xiong, B.D. Hoskins, M.W. Daniels, A. Madhavan, J.A. Liddle, J.J. McClelland, Y. Yang, J. Rupp, S.S. Nonnenmann, K.-T. Cheng, N. Gong, M.A. Lastras-Montaño, A.A. Talin, A. Salleo, B.J. Shastri, T.F. de Lima, P. Prucnal, A.N. Tait, Y. Shen, H. Meng, C. Roques-Carmes, Z. Cheng, H. Bhaskaran, D. Jariwala, H. Wang, J.M. Shainline, K. Segall, J.J. Yang, K. Roy, S. Datta, A. Raychowdhury, Nanotechnology 32 (2020). Berggren, Karl, et al. “Roadmap on Emerging Hardware and Technology for Machine Learning.” <i>Nanotechnology</i>, vol. 32, no. 1, 012002, IOP Publishing, 2020, doi:<a href="https://doi.org/10.1088/1361-6528/aba70f">10.1088/1361-6528/aba70f</a>. Berggren, Karl, Qiangfei Xia, Konstantin K Likharev, Dmitri B Strukov, Hao Jiang, Thomas Mikolajick, Damien Querlioz, et al. “Roadmap on Emerging Hardware and Technology for Machine Learning.” <i>Nanotechnology</i>. IOP Publishing, 2020. <a href="https://doi.org/10.1088/1361-6528/aba70f">https://doi.org/10.1088/1361-6528/aba70f</a>. Berggren, K., Xia, Q., Likharev, K. K., Strukov, D. B., Jiang, H., Mikolajick, T., … Raychowdhury, A. (2020). Roadmap on emerging hardware and technology for machine learning. <i>Nanotechnology</i>. IOP Publishing. <a href="https://doi.org/10.1088/1361-6528/aba70f">https://doi.org/10.1088/1361-6528/aba70f</a> Berggren K, Xia Q, Likharev KK, Strukov DB, Jiang H, Mikolajick T, Querlioz D, Salinga M, Erickson JR, Pi S, Xiong F, Lin P, Li C, Chen Y, Xiong S, Hoskins BD, Daniels MW, Madhavan A, Liddle JA, McClelland JJ, Yang Y, Rupp J, Nonnenmann SS, Cheng K-T, Gong N, Lastras-Montaño MA, Talin AA, Salleo A, Shastri BJ, de Lima TF, Prucnal P, Tait AN, Shen Y, Meng H, Roques-Carmes C, Cheng Z, Bhaskaran H, Jariwala D, Wang H, Shainline JM, Segall K, Yang JJ, Roy K, Datta S, Raychowdhury A. 2020. Roadmap on emerging hardware and technology for machine learning. Nanotechnology. 32(1), 012002. 215542026-03-30T12:22:47Z2026-04-15T06:55:27Z