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Hinton G. E., Salakhutdinov R. R., 2006, Reducing the dimensionality of data with neural networks, Science, Vol. 313, pp. 504-507DOI
Srivastava N., Hinton G. E., Krizhevsky A., Sutskever I., Salakhutdinov R., 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., Vol. 15, pp. 1929-1958URL
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., An-guelov D., Erhan D., Vanhoucke V., Rabinovich A., 2015, Going deeper with convolutions, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)URL
Gokmen T., Vlasov Y., 2016, Acceleration of deep neural network training with resistive cross-point devices: design considerations, Front. Neurosci., Vol. 10, pp. 333DOI
Kwon D., Lim S., Bae J.-H., Lee S.-T., Kim H., Seo Y.-T., Oh S., Kim J., Yeom K., Park B.-G., Lee J.-H., 2020, On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices, Front. Neurosci, Vol. 14, pp. 423DOI
Jeong D. S., Kim K. M., Kim S., Choi B. J., Hwang C. S., 2016, Memristors for Energy-Efficient New Computing Paradigms, Advanced Electronic Materials, Vol. 2, No. 9, pp. 1600090DOI
He K., Zhang X., Ren S., Sun J., 2016, Deep residual learning for image recognition, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)URL
Delvin J., Chang M.-W., Lee K., Toutanova K., 2019, BERT: Pre-training of Deep Bidirectional Trans-formers for Language Understanding, ArxivDOI
Yu S., 2018, Neuro-inspired computing with emerging nonvolatile memorys, Proc. of the IEEE, Vol. 106, No. 2, pp. 260-285DOI
Kwon D., Jung G., Shin W., Jeong Y., Hong S., Oh S., Bae J.-H., Park B.-G., Lee J.-H., 2020, Low-Power and reliable Gas Sensing System Based on Recurrent Neural Networks, Sensors and Actuators B: Chemical, pp. 129258DOI
Woo J., Kim J. H., Im J.-P., Moon S. E., 2020, Recent Advancements in Emerging Neuromor-phic Device Technologies, Advanced Intelligent Systems, Vol. 2, pp. 2000111DOI
Kim C.-H., Lim S., Woo S. Y., Kang W.-M., Seo Y.-T., Lee S.-T., Lee S., Kwon D., Oh S., Noh Y., Kim H., Kim J., Bae J.-H., Lee J.-H., p. , Emerging memory technologies for neuromorphic computing, Nanotechnology, Vol. 30, pp. 032001DOI
Hwang S., Chang J., Oh M.-H., Lee J.-H., Park B.-G., 2020, Impact of the Sub-Resting Membrane Potential on Accurate Inference in Spiking Neural Networks, Scientific Reports, Vol. 10, pp. 3515DOI
Dutta S., Schafer C., Gomez J., Ni K., Joshi S., Datta S., 2020, Supervised Learning in All FeFET-Based Spiking Neural Networks: Opportunities and Chal-lenges, Front. Neurosci., Vol. 14, pp. 634DOI
O’Connor P., Neil D., Liu S. C., Delbruck T., Pfeiffer M., 2013, Real-time classification and sensor fu-sion with a spiking deep belief network, Front. Neurosci., Vol. 7, pp. 178DOI
Rueckauer B., Lungu I.-A., Hu Y., Pfeiffer M., Liu S.-C., 2017, Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification, Front. Neurosci., Vol. 11, pp. 682DOI
Sung C.-L., Lue H.-T., Wei M.-L., Ho S.-Y., Hu H.-W., Du P.-Y., Chen W.-C., Lo C., Yeh T.-H., Wang K.-C., Lu C.-Y., 2020, A Novel Super-Steep Slope (~0.015mV/dec) Gate-Controlled Thyristor (GCT) Func-tional Memory Device to Support the Integrate-and-Fire Circuit for Spiking Neural Networks, IEEE In-ternational Electron Devices Meeting (IEDM)DOI
Indiveri G., 2003, A low-power adaptive inte-grate-and-fire neuron circuit, Proceedings of the 2003 International Symposium on Circuits and Sys-tems (ISCAS)DOI
Kang W.-M., Kim C.-H., Lee S., Woo S. Y., Bae J.-H., Park B.-G., Lee J.-H., 2019, A Spiking neural network with a global self-controller for unsupervised learning based on spike-timing-dependent plasticity using flash memory synaptic devices, In 2019 International Joint Conference on Neural Networks (IJCNN)DOI
Chua L., 2011, Resistance switching memo-ries are memristors, Appl. Phys. A., Vol. 102, pp. 765-783DOI
Burr G. W., et al. , 2016, Recent progress in phase-change memory technology, IEEE J. Emer. Sel. Topics Circuits Syst., Vol. 6, No. 2, pp. 146-162DOI
Sengupta A., et al. , 2016, Hybrid Spintron-ic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, Systems, Phys. Rev. Applied, Vol. 6, No. 6, pp. 064003DOI
Kwon M.-W., Baek M.-H., Hwang S., Park K., Jang T., Kim T., Lee J., Cho S., Park B.-G., 2018, Integrate-and-fire neuron circuit using positive feedback field effect transistor for low power operation, Journal of Applied Physics, Vol. 124, pp. 151903DOI
Chen C., Yang M., Liu S., Liu T., Zhu K., Zhao Y., Wang H., Huang Q., Huang R., 2019, Bio-Inspired Neurons Based on Novel Leaky-FeFET with Ultra-Low Hardware Cost and Advanced Functionality for All-Ferroelectric Neural Network, 2019 Symposium on VLSI Technology, pp. T136-T137DOI
Woo S. Y., Kwon D., Choi N., Kang W.-M., Seo Y.-T., Park M.-K., Bae J.-H., Park B.-G., Lee J.-H., 2020, Low-Power and High-Density Neuron Device for Simultaneous Processing of Excitatory and Inhibitory Signals in Neuromorphic Systems, IEEE Access, Vol. 8, pp. 202639-202647DOI
Indiveri G., et al. , 2011, Neuromorphic silicon neuron circuits, Front. Neurosci., Vol. 5, No. 73DOI
van Schaik A., 2001, Building blocks for elec-tronic spiking neural networks, Neural Networks, Vol. 14, pp. 617-628DOI
Indiveri G., Stefanini F., Chicca E., 2010, Spike-based learning with a generalized integrate and fire silicon neuron, Proceedings of the 2010 International Symposium on Circuits and Systems (ISCAS)DOI
Choi K.-B., Woo S. Y., Kang W.-M., Lee S., Kim C.-H., Bae J.-H., Lim S., Lee J.-H., 2018, A Split-Gate Positive Feedback Device With an Integrate-and-Fire Capability for a High-Density Low-Power Neuron Circuit, Front. Neurosci., Vol. 12, pp. 704DOI
Simonyan K., Zisserman A., 2015, Very Deep Convolutional Networks For Large-Scale Image Recognition, ArxivDOI
Stoliar et al. P., 2017, A Leaky-Integrate-and-Fire Neuron Analog Realized with a Mott Insulator, Adv. Func. Mater., Vol. 27, pp. 1604740DOI
Adda C., 2017, First demonstration of "Leaky Integrate and Fire" artificial neuron behavior on (V$_{\mathrm{0.95}}$Cr$_{\mathrm{0.05}}$)$_{2}$O$_{3}$ thin film, MRS Communications, Vol. 8, pp. 835-841DOI
Lin J., Yuan J., 2017, Capacitor-less RRAM-based stochastic neuron for event-based unsu-pervised learning, IEEE Bio. Cir. Sys. Conference (BioCAS), pp. 1-4DOI
Lin J., Yuan J., 2018, Analysis and Simulation of Capacitor-Less ReRAM-Based Stochastic Neurons for the in-Memory Spiking Neural Network, IEEE Trans. Bio. Cir. Sys., Vol. 12., No. 5, pp. 1004-1017DOI
Kwon M.-W., et al. , 2017, Integrate-and-Fire (I&F) Neuron Circuit Using Resistive-Switching Random Access Memory (RRAM), Journal of Nanosci-ence and Nanotechnology, Vol. 17, No. 5, pp. 3038-3041DOI
Panwar N., et al. , 2015, Effect of mor-phological change on unipolar and bipolar switching characteristics in Pr$_{\mathrm{0.7}}$Ca$_{\mathrm{0.3}}$MnO$_{3}$ based RRAM, in Proc. Mater. Res. Soc. Symp., Vol. 1729, pp. 47-52DOI
Lashkare S., et al , 2018, PCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks, IEEE Elec. Dev. Lett., Vol. 39, No. 4, pp. 484-487DOI
Tuma T., et al. , 2016, Stochastic phase-change neurons, Nature nanotechnology, Vol. 11, pp. 693-699DOI
Wright C. D., et al. , 2012, Beyond von-Neumann Computing with Nanoscale Phase-Change Memory Devices, Adv. Func. Mater., Vol. 23, pp. 2248-2254DOI
Fan D., et al. , 2015, STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neu-ron for Low-Power Artificial Neural Networks, IEEE Trans. Nano., Vol. 14, No. 6, pp. 1013-1023DOI
Zhang D., et al. , 2016, All Spin Artificial Neural Networks Based on Compound Spintronic Syn-apse and Neuron, IEEE Trans. on Bio. Cir. Sys., Vol. 10, No. 4, pp. 828-836DOI
Kondo K., 2018, A two-terminal perpen-dicular spin-transfer torque based artificial neuron, J. Phys. D: Appl. Phys., Vol. 51, pp. 504002DOI
Kim D. W., et al. , 2020, Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron, Front. Neurosci., Vol. 30, pp. 1-9DOI
Wu M.-H., et al. , 2019, Extremely Compact Integrate-and-Fire STT-MRAM Neuron: A Pathway to-ward All-Spin Artificial Deep Neural Network, Symposium on VLSI Technology, pp. T3-4DOI
Liang F.-X., et al. , 2019, Stochastic STT-MRAM Spiking Neuron Circuit, International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA), pp. 151-152DOI
Kurenkov A., et al. , 2019, Artificial Neuron and Synapse Realized in an Antiferromagnet/Ferromagnet Heterostructure Using Dynamics of Spin-Orbit Torque Switching, Adv. Mater., Vol. 31, No. 23, pp. 1900636DOI
Kwon M.-W., Park K., Baek M.-H., Lee J., Park B.-G., 2019, A Low-Energy High-Density Ca-pacitor-Less I&F Neuron Circuit Using Feedback FET Co-Integrated With CMOS, IEEE Journal of Electron Devices Society, Vol. 7, pp. 1080-1084DOI
Han J.-K., Seo M., Yu J.-M., Suh Y.-J., Choi Y.-K., 2020, A Single Transistor Neuron With Independently Accessed Double-Gate for Excitatory-Inhibitory Function and Tunable Firing Threshold Voltage, IEEE Electron Device Letters, Vol. 41, No. 8, pp. 1157-1160DOI
Han J.-K., Seo M., Kim W.-K., Kim M.-S., Kim S.-Y., Kim M.-S., Yun G.-J., Lee G.-B., Yu J.-M., 2020, Mimicry of Excitatory and In-hibitory Artificial Neuron With Leaky Integrate-and-Fire Function by a Single MOSFET, IEEE Electron Device Letters, Vol. 41, No. 2, pp. 208-211DOI
Saha A. K., Ni K., Dutta S., Datta S., Gupta S., 2019, Phase field modeling of domain dynamics and polarization accumulation in ferroelectric HZO, Applied Physics Letters, Vol. 114, pp. 202903DOI
Fang Y., Gomez J., Wang Z., Datta S., Khan S. I., Raychowdhury A., 2019, Neuro-Mimetic Dy-namics of a Ferroelectric FET-Based Spiking Neuron, IEEE Electron Device Letters, Vol. 40, No. 7, pp. 1213-1216DOI
Ni K., et al. , 2018, In-memory computing primitive for sensor data fusion in 28 nm HKMG FeFET technology, 2018 IEEE International Electron Devices Meeting (IEDM)DOI
Luo J., Yu L., Liu T., Yang M., Fu Z., Liang Z., Chen L., Chen C., Liu S., Wu S., Huang Q., Huang R., 2019, Capacitor-less Stochastic Leaky-FeFET Neuron of Excitatory and Inhibitory Connections for SNN with Reduced Hardware Cost, 2019 IEEE International Electron Devices Meeting (IEDM)DOI
Lee D., Kwak M., Moon K., Choi W., Park J., Yoo J., Song J., Lim S., Sung C., Banerjee W., Hwang H., 2019, Various Threshold Switching Devices for Integrate and Fire Neuron Applications, Advanced Electronic Materials, Vol. 5, pp. 1800866DOI