Publications

1. Huang, H., Niu, T., Yang, R., & Shi, L.*(2025). RAM2C: A Liberal Arts Educational Chatbot based on Retrieval-augmented Multi-role Multi-expert Collaboration. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 448-458).

2. Shi, L., Cong, Y., & Zhang, W. (2025). Dual Driven Approaches for General-Purpose Brain Inspired Computing. In 2025 9th IEEE Electron Devices Technology & Manufacturing Conference (EDTM) (pp. 01-03).

1. Zhang, W., Ma, S., Ji, X., Liu, X., Cong, Y., & Shi, L.* (2024). The development of general-purpose brain-inspired computing.Nature Electronics, 7(11), 954-965.

2. Baek, E., Song, S., Baek, C., Zhao, R., Shi, L.*, & Cannistraci, C. V.* (2024). Neuromorphic dendritic network computation with silent synapses for visual motion perception. Nature Electronics, 7(6), 454-465.

3. Yang, Z., Wang, T., Lin, Y., Chen, Y., Zeng, H., Pei, J., Wang, J., Liu, X., Zhou, Y., Zhang, J., Wang, X., Lv, X., Zhao, R.*, & Shi, L.* (2024). A vision chip with complementary pathways for open-world sensing. Nature, 629(8014), 1027-1033.

4. Liu, F., Zheng, H., Ma, S., Zhang, W., Liu, X., Chua, Y., Shi, L., & Zhao, R.* (2024). Advancing brain-inspired computing with hybrid neural networks. National Science Review, 11(5), nwae066.

5.Huang, H., He, L., Liu, F., Zhao, R., & Shi, L.* (2024, January). Neural Dynamics Pruning for Energy-Efficient Spiking Neural Networks. In IEEE International Conference on Multimedia and Expo (ICME 2024).

1. Zhang, W., & Shi, L.* (2023). Dual-stream Multi-Modal Graph Neural Network for Few-Shot Learning. In 6th International Conference on Multimedia Information Processing and Retrieval (MIPR 2023) (pp. 65-70).

2. Zheng, H., & Shi, L.* (2023). Coherence in Intelligent Systems. In AGI 2023: Artificial General Intelligence, 13921, 357-366.

3. Yu, F., Wu, Y., Ma, S., Xu, M., Li, H., Qu, H., Song, C., Wang, T., Zhao, R., & Shi, L.* (2023). Brain-inspired multimodal hybrid neural network for robot place recognition. Science Robotics, 8(78), eabm6996.

4. Pei, J., Deng, L., Ma, C., Liu, X., & Shi, L.* (2023). Multi-grained system integration for hybrid-paradigm brain-inspired computing. SCIENCE CHINA, 66(4), 142403.

1. 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.

2. 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.

3. 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.

4. Zheng, H., Lin, H., Zhao, R., & Shi, L.* (2022). Dance of SNN and ANN: solving binding problem by combining spike timing and reconstructive attention. Advances in Neural Information Processing Systems, 35, 31430-31443.

1. 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.

2. Tian, L., Wu, Z., Wu, S., & Shi, LP*. (2021). Hybrid neural state machine for neural network. Science China Information Sciences, 64(3), 1-13.

3. 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.

1. 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.

2. 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., & 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.

3. 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.

4. 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.

5. 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.

1. 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.

2. Li, H., & Shi, LP*. (2019). Robust event-based object tracking combining correlation filter and CNN representation. Frontiers in neurorobotics, 13, 82.

3. 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.

4. 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)

5. 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.

6. 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.

7. 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.

8. Li, H., Li, G., & Shi, LP*. (2019). Super-resolution of spatiotemporal event-stream image. Neurocomputing, 335, 206-214.

9. 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.

10. 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).

1. Shi, L. (2018, November). Brain Inspired Computing Devices, Chips and System. In 2018 Asia-Pacific Magnetic Recording Conference (APMRC) (pp. 1-1). IEEE.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. Jeong, H., & Shi, LP*. (2018). Memristor devices for neural networks. Journal of Physics D: Applied Physics, 52(2), 023003.

1. 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.

1. 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

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

1. 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.

2. 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.

3. 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.

4. Li, G., Ramanathan, K., Ning, N., Shi, L., & Wen, C. (2015). Memory dynamics in attractor networks. Computational intelligence and neuroscience, 2015.

5. 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

6. 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

7. Li, G., Pei, J., Wen, C., Li, Z., Zhao, G., & Shi, L. (2015, June). Hierarchical encoding of human working memory. In 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA) (pp. 866-871). IEEE.

8. Shi, L. (2015, July). Status, challenges and future trends of brain inspired computing. In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 7-7). IEEE.

9. Deng, L., Li, G., Deng, N., Wang, D., Zhang, Z., He, W., ... & Shi, L. (2015). Complex learning in bio-plausible memristive networks. Scientific reports, 5(1), 1-10.

10. Li, G., Hu, W., Xiao, G., Deng, L., Tang, P., Pei, J., & Shi, L. (2015). Minimum-cost control of complex networks. New Journal of Physics, 18(1), 013012.

1. Deng, L., Zhang, Z., Wang, D., Pei, J., & Shi, L. (2014, July). Ultra low power of artificial cognitive memory for brain-like computation. In 2014 IEEE International Nanoelectronics Conference (INEC) (pp. 1-4). IEEE.

2. Li, G., Wen, C., Wei, W., Xu, Y., Ding, J., Zhao, G., & Shi, L. (2014). Trace ratio criterion for feature extraction in classification. Mathematical Problems in Engineering, 2014.

3. He, W., Huang, K., Ning, N., Ramanathan, K., Li, G., Jiang, Y., ... & Pei, J. (2014). Enabling an integrated rate-temporal learning scheme on memristor. Scientific reports, 4(1), 1-6.

4. Tan, C. C., Zhao, R., Shi, L., & Chong, T. C. (2014). Implementation of nitrogen-doped titanium-tungsten tunable heater in phase change random access memory and its effects on device performance. Applied Physics Letters, 105(15), 153501.

5. Yang, H., Lee, H. K., Zhao, R., Shi, L., & Chong, T. C. (2014). Programming current density reduction for elevated-confined phase change memory with a self-aligned oxidation TiWOx heater. Applied Physics Letters, 105(21), 213509.

1. Yu, J., Tang, H., Li, H., & Shi, L. (2013). Dynamical properties of continuous attractor neural network with background tuning. Neurocomputing, 99, 439-447.

2. Li, G., Ning, N., Ramanathan, K., & Shi, L. (2013, April). Revised online learning with kernels for classification and regression. In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (pp. 275-279). IEEE.

3. Hu, J., Tang, H., Tan, K. C., Li, H., & Shi, L. (2013). A spike-timing-based integrated model for pattern recognition. Neural computation, 25(2), 450-472.

4. Zhuo VQ, Jiang Y, Li MH, Chua EK, Zhang Z, Pan JS, Zhao R, Shi LP, Chong TC, Robertson J. Band alignment between Ta2O5 and metals for resistive random access memory electrodes engineering. Applied Physics Letters. 2013 Feb 11;102(6):062106.

5. Huang, J. C., Bain, J. A., Song, W. D., Li, M. H., Shi, L. P., Schlesinger, T. E., & Chong, T. C. (2013). Assessing diffusion barriers for phase change memory devices using the magnetization of Fe. Applied Physics Letters, 102(25), 254102.

6. Li, G., Ning, N., Ramanathan, K., He, W., Pan, L. I., & Shi, L. (2013). Behind the magical numbers: hierarchical chunking and the human working memory capacity. International journal of neural systems, 23(04), 1350019.

7. Zhuo, V. Y. Q., Jiang, Y., Zhao, R., Shi, L. P., Yang, Y., Chong, T. C., & Robertson, J. (2013). Improved switching uniformity and low-voltage operation in TaOx-based RRAM using Ge reactive layer. IEEE electron device letters, 34(9), 1130-1132.

8. Chia Tan, C., Shi, L., Zhao, R., Guo, Q., Li, Y., Yang, Y., ... & Bain, J. A. (2013). Compositionally matched nitrogen-doped Ge2Sb2Te5/Ge2Sb2Te5 superlattice-like structures for phase change random access memory. Applied Physics Letters, 103(13), 133507.

1. Ning, N., Huang, K., & Shi, L. (2012, June). Artificial neuron with somatic and axonal computation units: Mathematical and neuromorphic models of persistent firing neurons. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.

2. Wang, W. J., Loke, D., Law, L. T., Shi, L. P., Zhao, R., Li, M. H., ... & Lacaita, A. L. (2012, December). Engineering grains of Ge2Sb2Te5 for realizing fast-speed, low-power, and low-drift phase-change memories with further multilevel capabilities. In 2012 International Electron Devices Meeting (pp. 31-3). IEEE.

3. Hongxin, Y., Luping, S., Koon, L. H., Rong, Z., & Chong, C. T. (2012). Endurance enhancement of elevated-confined phase change random access memory. Japanese Journal of Applied Physics, 51(2S), 02BD09.

4. Wang, W., Loke, D., Shi, L., Zhao, R., Yang, H., Law, L. T., ... & Lacaita, A. L. (2012). Enabling universal memory by overcoming the contradictory speed and stability nature of phase-change materials. Scientific reports, 2(1), 1-6.

5. Loke, D., Lee, T. H., Wang, W. J., Shi, L. P., Zhao, R., Yeo, Y. C., ... & Elliott, S. R. (2012). Breaking the speed limits of phase-change memory. Science, 336(6088), 1566-1569.

6. Li, M., Zhao, R., Law, L. T., Lim, K. G., & Shi, L. (2012). TiWOx interfacial layer for current reduction and cyclability enhancement of phase change memory. Applied Physics Letters, 101(7), 073502.

7. Zhao, R., Shi, L. P., Tan, C. C., Lee, H. K., Yang, H. X., Law, L. T., & Chong, T. C. (2012). Configuration effects of superlattice‐like phase change material structure. physica status solidi (b), 249(10), 1925-1931.

1. Fang LW, Zhao R, Yeo EG, Lim KG, Yang H, Shi L, Chong TC, Yeo YC. Phase change random access memory devices with nickel silicide and platinum silicide electrode contacts for integration with CMOS technology. Journal of The Electrochemical Society. 2011 Jan 4;158(3):H232.

2. Chin, H. C., Gong, X., Wang, L., Lee, H. K., Shi, L., & Yeo, Y. C. (2010). III–V Multiple-Gate Field-Effect Transistors With High-Mobility In(0.7)Ga(0.3)As Channel and Epi-Controlled Retrograde-Doped Fin. IEEE Electron Device Letters, 32(2), 146-148.

3. Song, W. D., Shi, L. P., & Chong, T. C. (2011). Magnetic Properties and Phase Change Features in Fe-Doped Ge–Sb–Te. Journal of nanoscience and nanotechnology, 11(3), 2648-2651.

4. Shi, L. P., Yi, K. J., Ramanathan, K., Zhao, R., Ning, N., Ding, D., & Chong, T. C. (2011). Artificial cognitive memory—changing from density driven to functionality driven. Applied Physics A, 102(4), 865-875.

5. Fang LW, Zhao R, Zhang Z, Pan J, Shi L, Chong TC, Yeo YC. Band offsets between SiO 2 and phase change materials in the (GeTe) x (Sb 2 Te 3) 1− x pseudobinary system. Applied Physics Letters. 2011 Mar 28;98(13):132103.

6. Fang, L. W. W., Zhao, R., Lim, K. G., Yang, H., Shi, L., Chong, T. C., & Yeo, Y. C. (2011). Phase change random access memory featuring silicide metal contact and high-κ interlayer for operation power reduction. Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena, 29(3), 032207.

7. Chua, E. K., Shi, L. P., Li, M. H., Zhao, R., Chong, T. C., Schlesinger, T. E., & Bain, J. A. (2011). Band alignment between GeTe and SiO 2/metals for characterization of junctions in nonvolatile resistance change elements. Applied Physics Letters, 98(23), 232104.

8. Loke, D., Shi, L., Wang, W., Zhao, R., Yang, H., Ng, L. T., ... & Yeo, Y. C. (2011). Ultrafast switching in nanoscale phase-change random access memory with superlattice-like structures. Nanotechnology, 22(25), 254019.

9. Ding, D., Bai, K., Song, W. D., Shi, L. P., Zhao, R., Ji, R., ... & Wu, P. (2011). Origin of ferromagnetism and the design principle in phase-change magnetic materials. Physical Review B, 84(21), 214416.

10. Huang, J. Q., Shi, L. P., Yeo, E. G., Yi, K. J., & Zhao, R. (2011). Electrochemical Metallization Resistive Memory Devices Using ZnS-SiO2 as a Solid Electrolyte. IEEE electron device letters, 33(1), 98-100.

1. Yeo, E. G., Shi, L., Zhao, R., Chong, C. T., & Adesida, I. (2010). Transient phase change analysis of scaling in phase change devices. International Journal of Nanoscience, 9(4), 351–354.

2. Deng, L., Zhang, Z., Wang, D., Pei, J., & Shi, L. (2010). Ultra low power of artificial cognitive memory for brain-like computation. Proceedings of 2010 International Symposium on VLSI Technology, System and Application (VLSI-TSA), 1–4.