香蕉久久这里只有精品-91国产自拍免费视频-免费A级毛片无码专区网站-无码八A片人妻少妇久久-特黄三级又长又粗又爽-国产精品人成在线播放-国产男女猛烈无遮挡性视频网站-丰满五十路熟女高清免费视频-欧美日韩午夜激情福利

J. Semicond. > 2021, Volume 42?>?Issue 1?> 010301

COMMENTS AND OPINIONS

Embracing the era of neuromorphic computing

Yanghao Wang1, Yuchao Yang1, 2, , Yue Hao3, and Ru Huang1, 2,

+ Author Affiliations

 Corresponding author: Yuchao Yang, Email: yuchaoyang@pku.edu.cn; Yue Hao, Email: yhao@xidian.edu.cn; Ru Huang, Email: ruhuang@pku.edu.cn

DOI: 10.1088/1674-4926/42/1/010301

PDF

Turn off MathJax



[1]
Kuhn T S. The structure of scientific revolutions. Chicago: University of Chicago Press, 1996
[2]
Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78, 1629 doi: 10.1109/5.58356
[3]
Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345, 668 doi: 10.1126/science.1254642
[4]
Davies M, Srinivasa N, Lin T H, et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 2018, 38(1), 82 doi: 10.1109/MM.2018.112130359
[5]
LeCun Y. Deep learning hardware: Past, present, and future. IEEE International Solid-State Circuits Conference, 2019, 12
[6]
Yang K, Duan Q, Wang Y, et al. Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems. Sci Adv, 2020, 6(33), eaba9901 doi: 10.1126/sciadv.aba9901
[7]
Lu Y, Li X, Yan L, et al. Accelerated local training of CNNs by optimized direct feedback alignment based on stochasticity of 4 Mb C-doped Ge2Sb2Te5 PCM chip in 40 nm node. IEEE International Electron Devices Meeting, 2020
[8]
Wang Z, Joshi S, Savel’ev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16(1), 101 doi: 10.1038/nmat4756
[9]
Duan Q, Jing Z, Zou X, et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat Commun, 2020, 11(1), 1 doi: 10.1038/s41467-019-13993-7
[10]
Kumar S, Strachan J P, Williams R S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature, 2017, 548(7667), 318 doi: 10.1038/nature23307
[11]
Kumar S, Williams R S, Wang Z. Third-order nanocircuit elements for neuromorphic engineering. Nature, 2020, 585(7826), 518 doi: 10.1038/s41586-020-2735-5
[12]
Zhang Y, Qu P, Ji Y, et al. A system hierarchy for brain-inspired computing. Nature, 2020, 586(7829), 378 doi: 10.1038/s41586-020-2782-y
[13]
Moon J, Ma W, Shin J H, et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat Electron, 2019, 2(10), 480 doi: 10.1038/s41928-019-0313-3
[14]
Torrejon J, Riou M, Araujo F A, et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature, 2017, 547(7664), 428 doi: 10.1038/nature23011
[15]
Talatchian P, Romera M, Tsunegi S, et al. Microwave neural processing and broadcasting with spintronic nano-oscillators. IEEE International Electron Devices Meeting, 2018, 27.4.1
[16]
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. arXiv: 1706.03762, 2017
Fig. 1.  (Color online) A possible roadmap of neuromorphic computing.

[1]
Kuhn T S. The structure of scientific revolutions. Chicago: University of Chicago Press, 1996
[2]
Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78, 1629 doi: 10.1109/5.58356
[3]
Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345, 668 doi: 10.1126/science.1254642
[4]
Davies M, Srinivasa N, Lin T H, et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 2018, 38(1), 82 doi: 10.1109/MM.2018.112130359
[5]
LeCun Y. Deep learning hardware: Past, present, and future. IEEE International Solid-State Circuits Conference, 2019, 12
[6]
Yang K, Duan Q, Wang Y, et al. Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems. Sci Adv, 2020, 6(33), eaba9901 doi: 10.1126/sciadv.aba9901
[7]
Lu Y, Li X, Yan L, et al. Accelerated local training of CNNs by optimized direct feedback alignment based on stochasticity of 4 Mb C-doped Ge2Sb2Te5 PCM chip in 40 nm node. IEEE International Electron Devices Meeting, 2020
[8]
Wang Z, Joshi S, Savel’ev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16(1), 101 doi: 10.1038/nmat4756
[9]
Duan Q, Jing Z, Zou X, et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat Commun, 2020, 11(1), 1 doi: 10.1038/s41467-019-13993-7
[10]
Kumar S, Strachan J P, Williams R S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature, 2017, 548(7667), 318 doi: 10.1038/nature23307
[11]
Kumar S, Williams R S, Wang Z. Third-order nanocircuit elements for neuromorphic engineering. Nature, 2020, 585(7826), 518 doi: 10.1038/s41586-020-2735-5
[12]
Zhang Y, Qu P, Ji Y, et al. A system hierarchy for brain-inspired computing. Nature, 2020, 586(7829), 378 doi: 10.1038/s41586-020-2782-y
[13]
Moon J, Ma W, Shin J H, et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat Electron, 2019, 2(10), 480 doi: 10.1038/s41928-019-0313-3
[14]
Torrejon J, Riou M, Araujo F A, et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature, 2017, 547(7664), 428 doi: 10.1038/nature23011
[15]
Talatchian P, Romera M, Tsunegi S, et al. Microwave neural processing and broadcasting with spintronic nano-oscillators. IEEE International Electron Devices Meeting, 2018, 27.4.1
[16]
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. arXiv: 1706.03762, 2017

    • Search

    Advanced Search >>

    GET CITATION

    shu

    Export: BibTex EndNote

    Article Metrics

    Article views: 6895 Times PDF downloads: 371 Times Cited by: 0 Times

    History

    Received: 30 December 2020 Revised: Online: Accepted Manuscript: 31 December 2020Uncorrected proof: 31 December 2020Published: 09 January 2021

    Catalog

      Email This Article

      User name:
      Email:*請(qǐng)輸入正確郵箱
      Code:*驗(yàn)證碼錯(cuò)誤
      Yanghao Wang, Yuchao Yang, Yue Hao, Ru Huang. Embracing the era of neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(1): 010301. doi: 10.1088/1674-4926/42/1/010301 ****Y H Wang, Y C Yang, Y Hao, R Huang, Embracing the era of neuromorphic computing[J]. J. Semicond., 2021, 42(1): 010301. doi: 10.1088/1674-4926/42/1/010301.
      Citation:
      Yanghao Wang, Yuchao Yang, Yue Hao, Ru Huang. Embracing the era of neuromorphic computing[J]. Journal of Semiconductors, 2021, 42(1): 010301. doi: 10.1088/1674-4926/42/1/010301 ****
      Y H Wang, Y C Yang, Y Hao, R Huang, Embracing the era of neuromorphic computing[J]. J. Semicond., 2021, 42(1): 010301. doi: 10.1088/1674-4926/42/1/010301.

      Embracing the era of neuromorphic computing

      DOI: 10.1088/1674-4926/42/1/010301
      • 1.

        Department of Micro/nanoelectronics, Peking University, Beijing 100871, China

      • 2.

        Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing 100871, China

      • 3.

        School of Microelectronics, Xidian University, Xi'an 710071, China

      More Information
      • Yuchao Yang：received his PhD from Tsinghua University. He is now an Assistant Professor in Department of Micro/nanoelectronics and serves as Director of Center for Brain Inspired Chips at Peking University. His research interests include memristors, neuromorphic computing, and in-memory computing
      • Yue Hao：is currently a Professor of Microelectronics and Solid State Electronics with Xidian University, Xi’an, China. His current interests include wide and untra-wide bandgap materials and devices, advanced CMOS devices and technology, semiconductor device reliability physics and failure mechanism, and organic electronics. Prof. Hao is a senior member of IEEE and member of the Chinese Academy of Sciences
      • Ru Huang：is currently a professor and vice president of Peking University. She is an elected academician of Chinese Academy of Science and IEEE Fellow. Her research interests include nano-scaled CMOS devices, ultra-low-power new devices, new device for neuromorphic computing, emerging memory technology and device variability/reliability. She is the Vice President of IEEE Electron Devices Society (EDS), the elected BoG member and the Chair of IEEE EDS SRC Region 10
      • Corresponding author: Email: yuchaoyang@pku.edu.cn; Email: yhao@xidian.edu.cn; Email: ruhuang@pku.edu.cn
      • Received Date: 2020-12-30
      • Published Date: 2021-01-10

      Catalog

        /

        DownLoad:  Full-Size Img  PowerPoint
        Return
        Return