A review of brain-like spiking neural network and its neuromorphic chip research_Journal of Biomedical Engineering

Authors：

ZHANG Huigang ^1,2,3 ,  XU Guizhi ^1,2,3 , GUO Jiarong ^1,2,3 , GUO Lei ^1,2,3

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China;
2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P.R.China;
3. Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin 300130, P.R.China;

Corresponding author：

XU Guizhi, Email: gzxu@hebut.edu.cn

Keywords：

spiking neural network; spiking neuron model; algorithm; neuromorphic chip; digital circuit

DOI：

10.7507/1001-5515.202011005

Video：

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Abstract Full text Figures/Tables Video References Cited by

Under the current situation of the rapid development of brain-like artificial intelligence and the increasingly complex electromagnetic environment, the most bionic and anti-interference spiking neural network has shown great potential in computing speed, real-time information processing, and spatiotemporal data processing. Spiking neural network is the core part of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure of biological neural network and the way of information transmission. This article first summarizes the advantages and disadvantages of the five models, and analyzes the characteristics of several network topologies. Then, it summarizes the spiking neural network algorithms. The unsupervised learning based on spike timing dependent plasticity (STDP) rules and four types of supervised learning algorithms are analyzed. Finally, the research on brain-like neuromorphic chips at home and abroad are reviewed. This paper aims to provide learning ideas and research directions for new colleagues in the field of spiking neural network.

Citation： ZHANG Huigang, XU Guizhi, GUO Jiarong, GUO Lei. A review of brain-like spiking neural network and its neuromorphic chip research. Journal of Biomedical Engineering, 2021, 38(5): 986-994, 1002. doi: 10.7507/1001-5515.202011005 Copy

1.	蒲慕明, 徐波, 谭铁牛. 脑科学与类脑研究概述. 中国科学院院刊, 2016, 31(7): 725-736, 714.
2.	蒲慕明. 脑科学的未来. 心理学通讯, 2019, 2(2): 80-83.
3.	张荣, 李伟平, 莫同. 深度学习研究综述. 信息与控制, 2018, 47(4): 385-397, 410.
4.	Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Netw, 1997, 10(9): 1659-1671.
5.	Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 1952, 117(4): 500-544.
6.	Abbott L F. Lapicque's introduction of the integrate-and-fire model neuron. Brain Res Bull, 2000, 50(5/6): 303-304.
7.	Burkitt A N. A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol Cybern, 2006, 95(1): 1-19.
8.	Gerstner W, Kistler W. Spiking neuron models: single neurons, populations, plasticity. Cambridges: Cambridge University Press, 2002: 1-477.
9.	Gerstner W. A framework for spiking neuron models: the spike response model. North-Holland: Handbook of Biological Physics, 2001, 4: 469-516.
10.	Izhikevich E M. Simple model of spiking neurons. IEEE Trans Neural Netw, 2003, 14(6): 1569-1572.
11.	Valadez-Godínez S, Sossa H, Santiago-Montero R. On the accuracy and computational cost of spiking neuron implementation. Neural Netw, 2020, 122: 196-217.
12.	Zuo L, Chen Y, Zhang L, et al. A spiking neural network with probability information transmission. Neurocomputing, 2020, 408: 1-12.
13.	Rumelhart D E, Hinton G E, Williams R J. Learning representations by back propagating errors. Nature, 1986, 323(6088): 533-536.
14.	Song S, Miller K D, Abbott L F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci, 2000, 3(9): 919-926.
15.	Hebb D O. The organization of behavior: a neuropsychological theory. London: Psychology Press, 2005: 1-365.
16.	Liu D, Yue S. Event-driven continuous STDP learning with deep structure for visual pattern recognition. IEEE Trans Cybern, 2019, 49(4): 1377-1390.
17.	Liu D, Bellotto N, Yue S. Deep spiking neural network for video-based disguise face recognition based on dynamic facial movements. IEEE Trans Neural Netw Learn Syst, 2020, 31(6): 1843-1855.
18.	Lee C, Srinivasan G, Panda P, et al. Deep spiking convolutional neural network trained with unsupervised spike timing dependent plasticity. IEEE Trans Cogn Dev Syst, 2019, 11(3): 384-394.
19.	Lee C, Panda P, Srinivasan G, et al. Training deep spiking convolutional neural networks with STDP-based unsupervised pre-training followed by supervised fine-tuning. Front Neurosci, 2018, 12: 435.
20.	Srinivasan G, Roy K. ReStoCNet: residual stochastic binary convolutional spiking neural network for memory-efficient neuromorphic computing. Front Neurosci, 2019, 13: 189.
21.	Ponulak F, Kasinski A. A supervised learning in spiking neural networks with ReSuMe: sequence learning, classifification, and spike shifting. Neural Comput, 2010, 22(2): 467-510.
22.	Taherkhani A, Belatreche A, Li Y, et al. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Trans Neural Netw Learn Syst, 2019, 29(11): 5396-5407.
23.	Frémaux N, Wulfram G. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front Neural Circuits, 2016, 9: 85.
24.	Brzosko Z, Zannone S, Schultz W, et al. Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. Elife, 2017, 6: e27756.
25.	Mozafari M, Ganjtabesh M, Nowzari-Dalini A, et al. Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks. Pattern Recognit, 2019, 94: 87-95.
26.	LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
27.	Bohte S, Kok J, Poutre H. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 2002, 48(1-4): 17-37.
28.	Hong C, Wei X, Wang J, et al. Training spiking neural networks for cognitive tasks: a versatile framework compatible with various temporal codes. IEEE Trans Neural Netw Learn Syst, 2020, 31(4): 1285-1296.
29.	Kim D, Kornijcuk V, Hwang C S et al. SPSNN: nth order sequence-predicting spiking neural network. IEEE Access, 2020, 8: 110523-110534.
30.	Shrestha S B, Orchard G. SLAYER: spike layer error reassignment in time// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc, 2018: 1412-1421.
31.	Wu Y J, Deng L, Li G Q, et al. Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci, 2018, 12: 331.
32.	Jin Y, Zhang W, Li P. Hybrid macro/micro level backpropagation for training deep spiking neural networks// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc, 2018: 7005-7015.
33.	Lee C, Sarwar S S, Panda P, et al. Enabling spike-based backpropagation for training deep neural network architectures. Front Neurosci, 2020, 14: 119.
34.	Martinelli F, Dellaferrera G, Mainar P, et al. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection// International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 8544-8548.
35.	Widrow B, Hoff M E. Adaptive switching circuits. Neurocomputing, 1960, 4: 126-134.
36.	Mohemmed A, Schliebs S. SPAN: spike pattern association neuron for learning spatio-temporal spike patterns. Int J Neural Syst, 2012, 22(4): 1250012.
37.	Yu Q, Tang H, Tan K C, et al. Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns. PloS ONE, 2013, 8(11): e78318.
38.	蔺想红, 王向文, 党小超. 基于脉冲序列核的脉冲神经元监督学习算法. 电子学报, 2016, 44(12): 2877-2886.
39.	Sengupta A, Ye Y, Wang R, et al. Going deeper in spiking neural networks: VGG and residual architectures. Front Neurosci, 2019, 13: 95.
40.	Srinivasan G, Lee C, Sengupta A, et al. Training deep spiking neural networks for energy-efficient neuromorphic computing// International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 8549-8553.
41.	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(6197): 668-673.
42.	Neckar A, Fok S, Benjamin B V, et al. Braindrop: a mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proc IEEE, 2018, 107(1): 144-164.
43.	Qiao N, Mostafa H, Corradi F, et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and128K synapses. Front Neurosci, 2015, 9: 141.
44.	Schemmel J, Brüderle D, Grübl A, et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling// 2010 IEEE International Symposium on Circuits and Systems (ISCAS). Paris: IEEE, 2010: 1947-1950.
45.	Schmitt S, Klaehn J, Bellec G, et al. Neuromorphic hardware in the loop: training a deep spiking network on the BrainScaleS wafer-scale system// 2017 International Joint Conference on Neural Networks (IJCNN). Alaska: IEEE, 2017: 2227-2234.
46.	Schemmel J. Towards the second generation brainscales system[R/OL]. (2018-2-27) [2021-8-17]. https://niceworkshop.org/nice-2018-agenda/.
47.	Andreas G, Billaudelle S, Cramer B, et al. Verification and design methods for the BrainScaleS neuromorphic hardware system. J Signal Process Syst, 2020, 92(11): 1277-1292.
48.	Moradi S, Qiao N, Stefanini F, et al. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans Biomed Circuits Syst, 2018, 12(99): 106-122.
49.	Thakur C S, Molin J L, Cauwenberghs G, et al. Large-scale neuromorphic spiking array processors: a quest to mimic the brain. Front Neurosci, 2018, 12: 891.
50.	DeBole M V, Taba B, Amir A, et al. TrueNorth: accelerating from zero to 64 million neurons in 10 years. Computer, 2019, 52(5): 20-29.
51.	Akopyan F, Sawada J, Cassidy A, et al. TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans Comput-Aid Des Integr Circuits Syst, 2015, 34(10): 1537-1557.
52.	Haessig G, Cassidy A, Alvarez R, et al. Spiking optical flow for event-based sensors using IBM's TrueNorth neurosynaptic system. IEEE Trans Biomed Circuits Syst, 2018, 12(4): 860-870.
53.	Palit I, Yang L, Ma Y, et al. Biomedical image segmentation using fully convolutional networks on TrueNorth// 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). Karlstad: IEEE, 2018: 375-380.
54.	Kiral-Kornek I, Mendis D, Nurse E S, et al. TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing// 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Korea: IEEE, 2017: 1648-1651.
55.	Furber S B, Lester D R, Plana L A, et al. Overview of the SpiNNaker system architecture. IEEE Trans Comput, 2013, 62(12): 2454-2467.
56.	Barchi F, Urgese G, Siino A, et al. Flexible on-line reconfiguration of multi-core neuromorphic platforms. IEEE Trans Emerg Top Comput, 2019, 9(2): 915-927.
57.	Hoppner S. Spinnaker2—Towards extremely efficient digital neuromorphics and multi-scale brain emulation[R/OL]. (2018-2-27) [2021-8-17]. https://niceworkshop.org/nice-2018-agenda/.
58.	Charlotte F, Martin L, Jean-Didier L, et al. A 0.086-mm² 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS. IEEE Trans Biomed Circuits Syst, 2019, 13(1): 145-158.
59.	Charlotte F, Jean-Didier L, David B. MorphIC: A 65-nm 738k-synapse/mm² quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Trans Biomed Circuits Syst., 2019, 13(5): 999-1010.
60.	Davies M, Srinivasa N, Lin T H, et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 2018, 38(1): 82-99.
61.	Blouw P, Choo X, Hunsberger E, et al. Benchmarking keyword spotting efficiency on neuromorphic hardware// Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop. New York: Association for Computing Machinery, 2019: 1-8.
62.	Shen J, Ma D, Gu Z, et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. Sci China Inf Sci, 2016, 59(2): 1-5.
63.	Ma D, Shen J, Gu Z, et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. J Syst Architect, 2017, 77: 43-51.
64.	Deng L, Wang G, Li G, et al. Tianjic: a unified and scalable chip bridging spike-based and continuous neural computation. IEEE J Solid-State Circuits, 2020, 55(8): 2228-2246.
65.	Pei J, Deng L, Song S, et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 2019, 572(7767): 106.

1. 蒲慕明, 徐波, 谭铁牛. 脑科学与类脑研究概述. 中国科学院院刊, 2016, 31(7): 725-736, 714.
2. 蒲慕明. 脑科学的未来. 心理学通讯, 2019, 2(2): 80-83.
3. 张荣, 李伟平, 莫同. 深度学习研究综述. 信息与控制, 2018, 47(4): 385-397, 410.
4. Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Netw, 1997, 10(9): 1659-1671.
5. Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 1952, 117(4): 500-544.
6. Abbott L F. Lapicque's introduction of the integrate-and-fire model neuron. Brain Res Bull, 2000, 50(5/6): 303-304.
7. Burkitt A N. A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol Cybern, 2006, 95(1): 1-19.
8. Gerstner W, Kistler W. Spiking neuron models: single neurons, populations, plasticity. Cambridges: Cambridge University Press, 2002: 1-477.
9. Gerstner W. A framework for spiking neuron models: the spike response model. North-Holland: Handbook of Biological Physics, 2001, 4: 469-516.
10. Izhikevich E M. Simple model of spiking neurons. IEEE Trans Neural Netw, 2003, 14(6): 1569-1572.
11. Valadez-Godínez S, Sossa H, Santiago-Montero R. On the accuracy and computational cost of spiking neuron implementation. Neural Netw, 2020, 122: 196-217.
12. Zuo L, Chen Y, Zhang L, et al. A spiking neural network with probability information transmission. Neurocomputing, 2020, 408: 1-12.
13. Rumelhart D E, Hinton G E, Williams R J. Learning representations by back propagating errors. Nature, 1986, 323(6088): 533-536.
14. Song S, Miller K D, Abbott L F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci, 2000, 3(9): 919-926.
15. Hebb D O. The organization of behavior: a neuropsychological theory. London: Psychology Press, 2005: 1-365.
16. Liu D, Yue S. Event-driven continuous STDP learning with deep structure for visual pattern recognition. IEEE Trans Cybern, 2019, 49(4): 1377-1390.
17. Liu D, Bellotto N, Yue S. Deep spiking neural network for video-based disguise face recognition based on dynamic facial movements. IEEE Trans Neural Netw Learn Syst, 2020, 31(6): 1843-1855.
18. Lee C, Srinivasan G, Panda P, et al. Deep spiking convolutional neural network trained with unsupervised spike timing dependent plasticity. IEEE Trans Cogn Dev Syst, 2019, 11(3): 384-394.
19. Lee C, Panda P, Srinivasan G, et al. Training deep spiking convolutional neural networks with STDP-based unsupervised pre-training followed by supervised fine-tuning. Front Neurosci, 2018, 12: 435.
20. Srinivasan G, Roy K. ReStoCNet: residual stochastic binary convolutional spiking neural network for memory-efficient neuromorphic computing. Front Neurosci, 2019, 13: 189.
21. Ponulak F, Kasinski A. A supervised learning in spiking neural networks with ReSuMe: sequence learning, classifification, and spike shifting. Neural Comput, 2010, 22(2): 467-510.
22. Taherkhani A, Belatreche A, Li Y, et al. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Trans Neural Netw Learn Syst, 2019, 29(11): 5396-5407.
23. Frémaux N, Wulfram G. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front Neural Circuits, 2016, 9: 85.
24. Brzosko Z, Zannone S, Schultz W, et al. Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. Elife, 2017, 6: e27756.
25. Mozafari M, Ganjtabesh M, Nowzari-Dalini A, et al. Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks. Pattern Recognit, 2019, 94: 87-95.
26. LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
27. Bohte S, Kok J, Poutre H. Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 2002, 48(1-4): 17-37.
28. Hong C, Wei X, Wang J, et al. Training spiking neural networks for cognitive tasks: a versatile framework compatible with various temporal codes. IEEE Trans Neural Netw Learn Syst, 2020, 31(4): 1285-1296.
29. Kim D, Kornijcuk V, Hwang C S et al. SPSNN: nth order sequence-predicting spiking neural network. IEEE Access, 2020, 8: 110523-110534.
30. Shrestha S B, Orchard G. SLAYER: spike layer error reassignment in time// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc, 2018: 1412-1421.
31. Wu Y J, Deng L, Li G Q, et al. Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci, 2018, 12: 331.
32. Jin Y, Zhang W, Li P. Hybrid macro/micro level backpropagation for training deep spiking neural networks// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc, 2018: 7005-7015.
33. Lee C, Sarwar S S, Panda P, et al. Enabling spike-based backpropagation for training deep neural network architectures. Front Neurosci, 2020, 14: 119.
34. Martinelli F, Dellaferrera G, Mainar P, et al. Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection// International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 8544-8548.
35. Widrow B, Hoff M E. Adaptive switching circuits. Neurocomputing, 1960, 4: 126-134.
36. Mohemmed A, Schliebs S. SPAN: spike pattern association neuron for learning spatio-temporal spike patterns. Int J Neural Syst, 2012, 22(4): 1250012.
37. Yu Q, Tang H, Tan K C, et al. Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns. PloS ONE, 2013, 8(11): e78318.
38. 蔺想红, 王向文, 党小超. 基于脉冲序列核的脉冲神经元监督学习算法. 电子学报, 2016, 44(12): 2877-2886.
39. Sengupta A, Ye Y, Wang R, et al. Going deeper in spiking neural networks: VGG and residual architectures. Front Neurosci, 2019, 13: 95.
40. Srinivasan G, Lee C, Sengupta A, et al. Training deep spiking neural networks for energy-efficient neuromorphic computing// International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 8549-8553.
41. 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(6197): 668-673.
42. Neckar A, Fok S, Benjamin B V, et al. Braindrop: a mixed-signal neuromorphic architecture with a dynamical systems-based programming model. Proc IEEE, 2018, 107(1): 144-164.
43. Qiao N, Mostafa H, Corradi F, et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and128K synapses. Front Neurosci, 2015, 9: 141.
44. Schemmel J, Brüderle D, Grübl A, et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling// 2010 IEEE International Symposium on Circuits and Systems (ISCAS). Paris: IEEE, 2010: 1947-1950.
45. Schmitt S, Klaehn J, Bellec G, et al. Neuromorphic hardware in the loop: training a deep spiking network on the BrainScaleS wafer-scale system// 2017 International Joint Conference on Neural Networks (IJCNN). Alaska: IEEE, 2017: 2227-2234.
46. Schemmel J. Towards the second generation brainscales system[R/OL]. (2018-2-27) [2021-8-17]. https://niceworkshop.org/nice-2018-agenda/.
47. Andreas G, Billaudelle S, Cramer B, et al. Verification and design methods for the BrainScaleS neuromorphic hardware system. J Signal Process Syst, 2020, 92(11): 1277-1292.
48. Moradi S, Qiao N, Stefanini F, et al. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans Biomed Circuits Syst, 2018, 12(99): 106-122.
49. Thakur C S, Molin J L, Cauwenberghs G, et al. Large-scale neuromorphic spiking array processors: a quest to mimic the brain. Front Neurosci, 2018, 12: 891.
50. DeBole M V, Taba B, Amir A, et al. TrueNorth: accelerating from zero to 64 million neurons in 10 years. Computer, 2019, 52(5): 20-29.
51. Akopyan F, Sawada J, Cassidy A, et al. TrueNorth: design and tool flow of a 65 mW 1 million neuron programmable neurosynaptic chip. IEEE Trans Comput-Aid Des Integr Circuits Syst, 2015, 34(10): 1537-1557.
52. Haessig G, Cassidy A, Alvarez R, et al. Spiking optical flow for event-based sensors using IBM's TrueNorth neurosynaptic system. IEEE Trans Biomed Circuits Syst, 2018, 12(4): 860-870.
53. Palit I, Yang L, Ma Y, et al. Biomedical image segmentation using fully convolutional networks on TrueNorth// 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). Karlstad: IEEE, 2018: 375-380.
54. Kiral-Kornek I, Mendis D, Nurse E S, et al. TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing// 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Korea: IEEE, 2017: 1648-1651.
55. Furber S B, Lester D R, Plana L A, et al. Overview of the SpiNNaker system architecture. IEEE Trans Comput, 2013, 62(12): 2454-2467.
56. Barchi F, Urgese G, Siino A, et al. Flexible on-line reconfiguration of multi-core neuromorphic platforms. IEEE Trans Emerg Top Comput, 2019, 9(2): 915-927.
57. Hoppner S. Spinnaker2—Towards extremely efficient digital neuromorphics and multi-scale brain emulation[R/OL]. (2018-2-27) [2021-8-17]. https://niceworkshop.org/nice-2018-agenda/.
58. Charlotte F, Martin L, Jean-Didier L, et al. A 0.086-mm² 12.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS. IEEE Trans Biomed Circuits Syst, 2019, 13(1): 145-158.
59. Charlotte F, Jean-Didier L, David B. MorphIC: A 65-nm 738k-synapse/mm² quad-core binary-weight digital neuromorphic processor with stochastic spike-driven online learning. IEEE Trans Biomed Circuits Syst., 2019, 13(5): 999-1010.
60. Davies M, Srinivasa N, Lin T H, et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 2018, 38(1): 82-99.
61. Blouw P, Choo X, Hunsberger E, et al. Benchmarking keyword spotting efficiency on neuromorphic hardware// Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop. New York: Association for Computing Machinery, 2019: 1-8.
62. Shen J, Ma D, Gu Z, et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. Sci China Inf Sci, 2016, 59(2): 1-5.
63. Ma D, Shen J, Gu Z, et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. J Syst Architect, 2017, 77: 43-51.
64. Deng L, Wang G, Li G, et al. Tianjic: a unified and scalable chip bridging spike-based and continuous neural computation. IEEE J Solid-State Circuits, 2020, 55(8): 2228-2246.
65. Pei J, Deng L, Song S, et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 2019, 572(7767): 106.

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