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2022
Wu, Y., Zhao, R., Zhu, J., Chen, F., Xu, M., Li, G., ... & Shi, LP*. (2022). Brain-inspired global-local learning incorporated with neuromorphic computing. Nature Communications, 13(1), 1-14.
Ma, S., Pei, J., Zhang, W., Wang, G., Feng, D., Yu, F., ... & Shi, LP*. (2022). Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots. Science Robotics, 7(67), eabk2948.
Zhao, R., Yang, Z., Zheng, H., Wu, Y., Liu, F., Wu, Z., ... & Shi, LP*. (2022). A framework for the general design and computation of hybrid neural networks. Nature Communications, 13(1), 1-12.
2021
Lim, D. H., Wu, S., Zhao, R., Lee, J. H., Jeong, H., & Shi, LP*. (2021). Spontaneous sparse learning for PCM-based memristor neural networks. Nature communications, 12(1), 1-14.
Tian, L., Wu, Z., Wu, S., & Shi, LP*. (2021). Hybrid neural state machine for neural network. Science China Information Sciences, 64(3), 1-13.
Wang, G., Ma, S., Wu, Y., Pei, J., Zhao, R., & Shi, LP*. (2021). End-to-end implementation of various hybrid neural networks on a cross-paradigm neuromorphic chip. Frontiers in Neuroscience, 15, 615279.
2020
Li, G., Tang, P., Chen, X., Xiao, G., Meng, M., Ma, C., & Shi, LP*. (2020). Target control and expandable target control of complex networks. Journal of the Franklin Institute, 357(6), 3541-3564.
Deng, L., Wang, G., Li, G., Li, S., Liang, L., Zhu, M., ... & Shi, LP*. (2020). Tianjic: A unified and scalable chip bridging spike-based and continuous neural computation. IEEE Journal of Solid-State Circuits, 55(8), 2228-2246.
Wang, Y., Wu, S., Tian, L., & Shi, LP*. (2020). SSM: a high-performance scheme for in situ training of imprecise memristor neural networks. Neurocomputing, 407, 270-280.
Zhang, Y*.; Qu, P.; Ji, Y.; Zhang, W.; Gao, G.; Wang, G.; Song, S.; Li, G.; Chen, W.; Zheng, W.; Chen, F.; Pei, J.; Zhao, R.; Zhao, M.; and Shi, LP*。(2020). A system hierarchy for brain-inspired computing, Nature, 586(7829): 378-384.
Deng, L.; Wang, G.; Li, G.; Li, S.; Liang, L.; Zhu, M.; Wu, Y.; Yang, Z.; Zou, Z.; Pei, J.; Wu, Z.; Hu, X.; Ding, Y.; He, W.; Xie, Y.; and Shi, LP* (2020). Tianjic: A Unified and Scalable Chip Bridging Spike-Based and Continuous Neural Computation. IEEE Journal of Solid-State Circuits, 55(8): 2228-2246.
施路平; 裴京; 赵蓉; (2020). 面向人工通用智能的类脑计算, 人工智能：类脑计算与脑科学, 1: 6-15.
2019
Wang, Y., Zhang, Z., Li, H., & Shi, LP*. (2019). Realizing bidirectional threshold switching in Ag/Ta2O5/Pt diffusive devices for selector applications. Journal of Electronic Materials, 48(1), 517-525.
Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., & Shi, LP*. (2019, July). Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 1311-1318).
Wu, S., Wang, G., Tang, P., Chen, F., & Shi, LP*. (2019). Convolution with even-sized kernels and symmetric padding. Advances in Neural Information Processing Systems, 32.
Li, H., Li, G., & Shi, LP*. (2019). Super-resolution of spatiotemporal event-stream image. Neurocomputing, 335, 206-214.
Lee, J. H., Lim, D. H., Jeong, H., Ma, H., & Shi, LP*. (2019). Exploring cycle-to-cycle and device-to-device variation tolerance in MLC storage-based neural network training. IEEE Transactions on Electron Devices, 66(5), 2172-2178.
Wu, S., Li, G., Deng, L., Liu, L., Wu, D., Xie, Y., & Shi, LP*. (2018). $ L1 $-norm batch normalization for efficient training of deep neural networks. IEEE transactions on neural networks and learning systems, 30(7), 2043-2051.
Wang, Y., Zhang, Z., Xu, M., Yang, Y., Ma, M., Li, H., ... & Shi, LP*. (2019). Self-doping memristors with equivalently synaptic ion dynamics for neuromorphic computing. ACS applied materials & interfaces, 11(27), 24230-24240.
Pei, J.; Deng, L.; Song, S.; Zhao, M.; Zhang, Y.; Wu, S.; Wang, G.; Zou, Z.; Wu, Z.; He, W.; Chen, F.; Deng, N.; Wu, S.; Wang, Y.; Wu, Y.; Yang, Z.; Ma, C.; Li, G.; Han, W.; Li, H.; Wu, H.; Zhao, R.; Xie, Y.; and Shi, LP* (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture, Nature, 572(7767): 106-111. (封面文章)
Li, G., Chen, X., Tang, P., Xiao, G., Wen, C., & Shi, LP*. (2019). Target control of directed networks based on network flow problems. IEEE Transactions on Control of Network Systems, 7(2), 673-685.
Li, H., & Shi, LP*. (2019). Robust event-based object tracking combining correlation filter and CNN representation. Frontiers in neurorobotics, 13, 82.
Wang, Y., Zhang, Z., Li, H., & Shi, LP*. (2019). Realizing bidirectional threshold switching in Ag/Ta2O5/Pt diffusive devices for selector applications. Journal of Electronic Materials, 48(1), 517-525.
2018
Shi, L. (2018, November). Brain Inspired Computing Devices, Chips and System. In 2018 Asia-Pacific Magnetic Recording Conference (APMRC) (pp. 1-1). IEEE.
Li, G., Deng, L., Tian, L., Cui, H., Han, W., Pei, J., & Shi, LP*. (2018). Training deep neural networks with discrete state transition. Neurocomputing, 272, 154-162.
Li, G., Deng, L., Xiao, G., Tang, P., Wen, C., Hu, W., Pei, J., Shi, L. and Stanley, H.E., 2018. Enabling controlling complex networks with local topological information. Scientific reports, 8(1), pp.1-10.
Wu, Y., Deng, L., Li, G., Zhu, J., & Shi, LP*. (2018). Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in neuroscience, 12, 331.
Zhang, Z., Wang, Y., Li, H., Wu, Y., Wang, G., & Shi, LP*. (2018). Engineering the Synaptic Kinetic Process into Memristive Device. Advanced Electronic Materials, 4(6), 1800096.
Li, H., Li, G., Ji, X., & Shi, LP*. (2018). Deep representation via convolutional neural network for classification of spatiotemporal event streams. Neurocomputing, 299, 1-9.
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., ... & Shi, LP*. (2018). Highly compact artificial memristive neuron with low energy consumption. Small, 14(51), 1802188.
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., ... & Shi, LP*. (2018). Highly compact artificial memristive neuron with low energy consumption. Small, 14(51), 1802188.
Jeong, H., & Shi, LP*. (2018). Memristor devices for neural networks. Journal of Physics D: Applied Physics, 52(2), 023003.
2017
Li, H., Liu, H., Ji, X., Li, G., & Shi, LP*. (2017). Cifar10-dvs: an event-stream dataset for object classification. Frontiers in neuroscience, 11, 309.
2016
Li, H., Li, G., & Shi, LP*. (2016, November). Classification of spatiotemporal events based on random forest. In International Conference on Brain Inspired Cognitive Systems (pp. 138-148). Springer, Cham
Li, G., Deng, L., Xu, Y., Wen, C., Wang, W., Pei, J., & Shi, LP*. (2016). Temperature based restricted Boltzmann machines. Scientific reports, 6(1), 1-12.
Li, H., Zhang, Z., & Shi, LP*. (2016). Identifying and engineering the electronic properties of the resistive switching interface. Journal of Electronic Materials, 45(2), 1142-1153.
Zhang, Z., Li, H., & Shi, LP* (2016). Correlation and ordering of defects in the formation of conducting nanofilaments. Journal of Physics D: Applied Physics, 49(12), 125303.
Li, H., Pei, J., & Shi, LP*. (2016). Electronic structure and spin configuration trends of single transition metal impurity in phase change material. Journal of Electronic Materials, 45(10), 5158-5169.
Li, G., Deng, L., Wang, D., Wang, W., Zeng, F., Zhang, Z., ... & Shi, LP*. (2016). Hierarchical chunking of sequential memory on neuromorphic architecture with reduced synaptic plasticity. Frontiers in computational neuroscience, 10, 136.
2015
Ning, N., Li, G., He, W., Huang, K., Pan, L., Ramanathan, K., ... & Shi, L. (2015). Modeling neuromorphic persistent firing networks. International Journal of Intelligence Science, 5(02), 89.
Shi, L., Pei, J., Deng, N., Wang, D., Deng, L., Wang, Y., ... & Ma, C. (2015, December). Development of a neuromorphic computing system. In 2015 IEEE international electron devices meeting (IEDM) (pp. 4-3). IEEE.
Yang, H., Shi, L., Zhao, R., Lee, H. K., Li, J., Lim, K. G., ... & Chong, T. C. (2014). Growth-Dominant Superlattice-Like Medium and Its Application in Phase Change Memory. ECS Journal of Solid State Science and Technology, 4(3), N13.
Li, G., Ramanathan, K., Ning, N., Shi, L., & Wen, C. (2015). Memory dynamics in attractor networks. Computational intelligence and neuroscience, 2015.
Hongxin, Y., Luping, S., Koon, L. H., Rong, Z., Minghua, L., Jianming, L., ... & Chong, C. T. (2015). Multi-level lateral phase change memory based on N-doped Sb70Te30 super-lattice like structure. ECS Journal of Solid State Science and Technology, 4(12), N147
Shi, L. (2015, September). A study of the nature of light by comparing real and digital universes. In The Nature of Light: What are Photons? VI (Vol. 9570, pp. 125-134). SPIE
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