Roadmap on emerging hardware and technology for machine learning
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 li...
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| Published in: | Nanotechnology Vol. 32; no. 1; pp. 012002 - 12046 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
IOP Publishing
01.01.2021
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| ISSN: | 0957-4484, 1361-6528, 1361-6528 |
| Online Access: | Get full text |
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| 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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| AbstractList | 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. 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.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. |
| Author | Raychowdhury, Arijit Nonnenmann, Stephen S McClelland, Jabez J Xiong, Feng Segall, Kenneth Shainline, Jeffrey M Roy, Kaushik Erickson, John R Yang, J Joshua Jiang, Hao Strukov, Dmitri B Madhavan, Advait Berggren, Karl Pi, Shuang Hoskins, Brian D Prucnal, Paul Cheng, Zengguang Bhaskaran, Harish Talin, A Alec Shastri, Bhavin J Roques-Carmes, Charles de Lima, Thomas Ferreira Yang, Yuchao Gong, Nanbo Meng, Huaiyu Xia, Qiangfei Shen, Yichen Lin, Peng Querlioz, Damien Daniels, Matthew W Salleo, Alberto Datta, Suman Likharev, Konstantin K Chen, Yu Mikolajick, Thomas Salinga, Martin Lastras-Montaño, Miguel Angel Liddle, James A Xiong, Shisheng Cheng, Kwang-Ting Li, Can Rupp, Jennifer Tait, Alexander N Jariwala, Deep Wang, Han |
| Author_xml | – sequence: 1 givenname: Karl orcidid: 0000-0001-7453-9031 surname: Berggren fullname: Berggren, Karl email: berggren@mit.edu organization: Research Laboratory of Electronics, Massachusetts Institute of Technology , Cambridge, MA 02139, United States of America – sequence: 2 givenname: Qiangfei orcidid: 0000-0003-1436-8423 surname: Xia fullname: Xia, Qiangfei email: qxia@umass.edu organization: University of Massachusetts Department of Electrical and Computer Engineering, Amherst, MA, United States of America – sequence: 3 givenname: Konstantin K surname: Likharev fullname: Likharev, Konstantin K organization: Stony Brook University , Stony Brook, NY 11794, Unites States – sequence: 4 givenname: Dmitri B surname: Strukov fullname: Strukov, Dmitri B organization: University of California at Santa Barbara Department of Electrical and Computer Engineering, Santa Barbara, CA 93106, United States of America – sequence: 5 givenname: Hao surname: Jiang fullname: Jiang, Hao organization: School of Engineering & Applied Science Yale University , CT, United States of America – sequence: 6 givenname: Thomas orcidid: 0000-0003-3814-0378 surname: Mikolajick fullname: Mikolajick, Thomas organization: NaMLab gGmbH and TU Dresden , Germany – sequence: 7 givenname: Damien surname: Querlioz fullname: Querlioz, Damien organization: Université Paris-Saclay , CNRS, France – sequence: 8 givenname: Martin orcidid: 0000-0002-2228-6244 surname: Salinga fullname: Salinga, Martin organization: Institut für Materialphysik , Westfälische Wilhelms-Universität Münster, Germany – sequence: 9 givenname: John R surname: Erickson fullname: Erickson, John R organization: University of Pittsburgh Department of Electrical and Computer Engineering, Pittsburgh, PA 15261, United States of America – sequence: 10 givenname: Shuang surname: Pi fullname: Pi, Shuang organization: Lam Research , Fremont, CA, United States of America – sequence: 11 givenname: Feng surname: Xiong fullname: Xiong, Feng organization: University of Pittsburgh Department of Electrical and Computer Engineering, Pittsburgh, PA 15261, United States of America – sequence: 12 givenname: Peng surname: Lin fullname: Lin, Peng organization: Research Laboratory of Electronics, Massachusetts Institute of Technology , Cambridge, MA 02139, United States of America – sequence: 13 givenname: Can orcidid: 0000-0003-3795-2008 surname: Li fullname: Li, Can organization: The University of Hong Kong Department of Electrical and Electronic Engineering, , China Hong Kong SAR – sequence: 14 givenname: Yu surname: Chen fullname: Chen, Yu organization: School of information science and technology, Fudan University , Shanghai, People's Republic of China – sequence: 15 givenname: Shisheng surname: Xiong fullname: Xiong, Shisheng organization: School of information science and technology, Fudan University , Shanghai, People's Republic of China – sequence: 16 givenname: Brian D surname: Hoskins fullname: Hoskins, Brian D organization: Physical Measurements Laboratory, National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of America – sequence: 17 givenname: Matthew W orcidid: 0000-0002-3390-4714 surname: Daniels fullname: Daniels, Matthew W organization: Physical Measurements Laboratory, National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of America – sequence: 18 givenname: Advait surname: Madhavan fullname: Madhavan, Advait organization: Institute for Research in Electronics and Applied Physics, University of Maryland, College Park , MD, United States of America – sequence: 19 givenname: James A surname: Liddle fullname: Liddle, James A organization: Physical Measurements Laboratory, National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of America – sequence: 20 givenname: Jabez J surname: McClelland fullname: McClelland, Jabez J organization: Physical Measurements Laboratory, National Institute of Standards and Technology , Gaithersburg, MD 20899, United States of America – sequence: 21 givenname: Yuchao orcidid: 0000-0003-4674-4059 surname: Yang fullname: Yang, Yuchao organization: School of Electronics Engineering and Computer Science, Peking University , Beijing, People's Republic of China – sequence: 22 givenname: Jennifer surname: Rupp fullname: Rupp, Jennifer organization: ETHZ Department of Materials Electrochemical Materials, Hönggerbergring 64, Zürich 8093, Switzerland – sequence: 23 givenname: Stephen S surname: Nonnenmann fullname: Nonnenmann, Stephen S organization: University of Massachusetts-Amherst Department of Mechanical & Industrial Engineering, MA, United States of America – sequence: 24 givenname: Kwang-Ting orcidid: 0000-0002-3885-4912 surname: Cheng fullname: Cheng, Kwang-Ting organization: School of Engineering, Hong Kong University of Science and Technology , Clear Water Bay, Kowloon, Hong Kong, People's Republic of China – sequence: 25 givenname: Nanbo orcidid: 0000-0002-9797-5124 surname: Gong fullname: Gong, Nanbo organization: IBM T J Watson Research Center , Yorktown Heights, NY 10598, United States of America – sequence: 26 givenname: Miguel Angel surname: Lastras-Montaño fullname: Lastras-Montaño, Miguel Angel organization: Universidad Autónoma de San Luis Potosí Instituto de Investigación en Comunicación Óptica, Facultad de Ciencias, México – sequence: 27 givenname: A Alec surname: Talin fullname: Talin, A Alec organization: Sandia National Laboratories , Livermore, CA 94551, United States of America – sequence: 28 givenname: Alberto surname: Salleo fullname: Salleo, Alberto organization: Stanford University Department of Materials Science and Engineering, Stanford, California, United States of America – sequence: 29 givenname: Bhavin J orcidid: 0000-0001-5040-8248 surname: Shastri fullname: Shastri, Bhavin J organization: Engineering Physics & Astronomy, Queen's University Department of Physics, Kingston ON KL7 3N6, Canada – sequence: 30 givenname: Thomas Ferreira surname: de Lima fullname: de Lima, Thomas Ferreira organization: Princeton University Department of Electrical Engineering, Princeton, NJ 08544, United States of America – sequence: 31 givenname: Paul surname: Prucnal fullname: Prucnal, Paul organization: Princeton University Department of Electrical Engineering, Princeton, NJ 08544, United States of America – sequence: 32 givenname: Alexander N surname: Tait fullname: Tait, Alexander N organization: Physical Measurement Laboratory, National Institute of Standards and Technology (NIST) , Boulder, CO 80305, United States of America – sequence: 33 givenname: Yichen surname: Shen fullname: Shen, Yichen organization: Lightelligence , 268 Summer Street, Boston, MA 02210, United States of America – sequence: 34 givenname: Huaiyu surname: Meng fullname: Meng, Huaiyu organization: Lightelligence , 268 Summer Street, Boston, MA 02210, United States of America – sequence: 35 givenname: Charles surname: Roques-Carmes fullname: Roques-Carmes, Charles organization: Research Laboratory of Electronics, Massachusetts Institute of Technology , Cambridge, MA 02139, United States of America – sequence: 36 givenname: Zengguang orcidid: 0000-0002-2204-3429 surname: Cheng fullname: Cheng, Zengguang organization: State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University , Shanghai 200433, People's Republic of China – sequence: 37 givenname: Harish surname: Bhaskaran fullname: Bhaskaran, Harish organization: University of Oxford Department of Materials, Oxford OX1 3PH, United Kingdom – sequence: 38 givenname: Deep orcidid: 0000-0002-3570-8768 surname: Jariwala fullname: Jariwala, Deep organization: University of Pennsylvania Department of Electrical and Systems Engineering, Philadelphia PA 19104, United States of America – sequence: 39 givenname: Han surname: Wang fullname: Wang, Han organization: University of Southern California , Los Angeles, CA 90089, United States of America – sequence: 40 givenname: Jeffrey M orcidid: 0000-0002-6102-5880 surname: Shainline fullname: Shainline, Jeffrey M organization: Physical Measurement Laboratory, National Institute of Standards and Technology (NIST) , Boulder, CO 80305, United States of America – sequence: 41 givenname: Kenneth surname: Segall fullname: Segall, Kenneth organization: Colgate University Department of Physics and Astronomy, NY 13346, United States of America – sequence: 42 givenname: J Joshua orcidid: 0000-0001-8242-7531 surname: Yang fullname: Yang, J Joshua organization: University of Massachusetts Department of Electrical and Computer Engineering, Amherst, MA, United States of America – sequence: 43 givenname: Kaushik surname: Roy fullname: Roy, Kaushik organization: School of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907, United States of America – sequence: 44 givenname: Suman surname: Datta fullname: Datta, Suman organization: University of Notre Dame , Notre Dame, IN 46556, United States of America – sequence: 45 givenname: Arijit surname: Raychowdhury fullname: Raychowdhury, Arijit organization: Georgia Institute of Technology , Atlanta, GA 30332, United States of America |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32679577$$D View this record in MEDLINE/PubMed |
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