Artificial Neurons Based on Memristors for Spiking Neural Networks

Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

Abstract:

Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to their high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO2-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO2-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: Leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity.

References:

[1] M Maass. W, “Networks of Spiking Neurons: The Third Generation of Neural Network Models,” Neural networks, vol. 10, no. 9, pp. 1659-1671, 1997.
[2] D. B. Strukov, G. S. Snider, D. R. Stewart and R. S. Williams, “The missing memristor found,” Nature, vol. 453, no. 7191, pp. 80-3, May 1 2008.
[3] H. Jeong and L. Shi, “Memristor devices for neural networks,” Journal of Physics D: Applied Physics, vol. 52, no. 2, 2019.
[4] E. Zhou, L. Fang and B. Yang, “Memristive Spiking Neural Networks Trained with Unsupervised STDP,” Electronics, vol. 7, no. 12, 2018.
[5] K. Roy, A. Jaiswal and P.Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature, vol. 575, no. 7784, pp. 607-617, 2019.
[6] Pei. J, Deng. L, Song. S et al, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature, vol. 572, no. 7767, pp. 106-111, 2019.
[7] B. J. Choi, D. S. Jeong, S. K. Kim, C. Rohde, S. Choi, J. H. Oh et al., “Resistive switching mechanism of TiO2 thin films grown by atomic-layer deposition,” Journal of Applied Physics, vol. 98, no. 3, 2005.
[8] X. Yan, J. Zhao, S. Liu, Z. Zhou, Q. Liu, J. Chen et al., “Memristor with Ag-Cluster-Doped TiO2Films as Artificial Synapse for Neuroinspired Computing,” Advanced Functional Materials, vol. 28, no. 1, 2018.
[9] C. Rohde, B. J. Choi, D. S. Jeong, S. Choi, J.-S. Zhao and C. S. Hwang, “Identification of a determining parameter for resistive switching of TiO2 thin films,” Applied Physics Letters, vol. 86, no. 26, 2005.
[10] D. H. Kwon, K. M. Kim, J. H. Jang, J. M. Jeon, M. H. Lee, G. H. Kim et al., “Atomic structure of conducting nanofilaments in TiO2 resistive switching memory,” Nat Nanotechnol, vol. 5, no. 2, pp. 148-53, Feb 2010.
[11] P. U. Diehl and M. Cook, “Unsupervised learning of digit recognition using spike-timing-dependent plasticity,” Front Comput Neurosci, vol. 9, p. 99, 2015.
[12] T. Masquelier and S. J. Thorpe, “Unsupervised learning of visual features through Spike Timing Dependent Plasticity,” PLoS Computational Biology, vol. preprint, no. 2007, 2005.
[13] E. O. Neftci, H. Mostafa and F. Zenke, “Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 51-63, 2019.
[14] K. Roy, A. Jaiswal and P. Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature, vol. 575, no. 7784, pp. 607-617, Nov 2019.
[15] Z. Hajiabadi and M. Shalchian, “Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks,” Journal of Computational Electronics, vol. 20, no. 4, pp. 1625-1636, 2021.
[16] N. Zheng and P. Mazumder, “Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity,” IEEE Transactions on Nanotechnology, vol. 17, no. 3, pp. 520-532, 2018.
[17] R. Midya, Z. Wang, S. Asapu, S. Joshi, Y. Li, Y. Zhuo et al., “Artificial Neural Network (ANN) to Spiking Neural Network (SNN) Converters Based on Diffusive Memristors,” Advanced Electronic Materials, vol. 5, no. 9, 2019.
[18] D. Querlioz, O. Bichler, P. Dollfus and C. Gamrat, “Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices,” IEEE Transactions on Nanotechnology, vol. 12, no. 3, pp. 288-295, 2013.
[19] E. Stromatias, M. Soto, T. Serrano-Gotarredona and B. Linares-Barranco, “An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data,” Front Neurosci, vol. 11, p. 350, 2017.
[20] X. Zhang, S. Liu, X. Zhao, F. Wu, Q. Wu, W. Wang et al., “Emulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor,” IEEE Electron Device Letters, vol. 38, no. 9, pp. 1208-1211, 2017.
[21] X. Yan, K. Wang, J. Zhao, Z. Zhou, H. Wang, J. Wang et al., “A New Memristor with 2D Ti3 C2 Tx MXene Flakes as an Artificial Bio-Synapse,” Small, vol. 15, no. 25, p. e1900107, Jun 2019.
[22] J.-Q. Yang, R. Wang, Z.-P. Wang, Q.-Y. Ma, J.-Y. Mao, Y. Ren et al., “Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks,” Nano Energy, vol. 74, 2020.
[23] T. Guo, B. Sun, S. Ranjan, Y. Jiao, L. Wei, Y. N. Zhou et al., “From Memristive Materials to Neural Networks,” ACS Appl Mater Interfaces, vol. 12, no. 49, pp. 54243-54265, Dec 9 2020.