Embracing the era of neuromorphic computing

Yanghao Wang; Yuchao Yang; Yue Hao; Ru Huang

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

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

COMMENTS AND OPINIONS

Embracing the era of neuromorphic computing

Yanghao Wang¹, Yuchao Yang^{1, 2,}, Yue Hao^3, and Ru Huang^{1, 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

References

[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.

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[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