Roadmap on emerging hardware and technology for machine learning
Karl K. 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. McClelland, Yuchao Yang, Jennifer L. M. Rupp, Stephen S. Nonnenmann, Kwang‐Ting Cheng, Nanbo Gong, Miguel Ángel Lastras-Montaño, A. Alec Talin, Alberto Salleo, Bhavin J. Shastri, Thomas Ferreira de Lima, Paul R. Prucnal, Alexander N. Tait, Yichen Shen, Huaiyu Meng, Charles Roques‐Carmes, Zengguang Cheng, Harish Bhaskaran, Deep Jariwala, Han Wang, Jeffrey M. Shainline, K. Segall, J. Joshua Yang, Kaushik Roy, Suman Datta, Arijit Raychowdhury
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.