연구동향 분석

Demonstration of Hardware-based Spiking Neural Network Using an AND-type Flash Memory Array Architecture = AND-형 플래시 메모리 어레이를 활용한 하드웨어 기반 스파이킹 뉴럴 네트워크 구현

강원묵 2022년

논문상세정보
인용/피인용
' Demonstration of Hardware-based Spiking Neural Network Using an AND-type Flash Memory Array Architecture = AND-형 플래시 메모리 어레이를 활용한 하드웨어 기반 스파이킹 뉴럴 네트워크 구현' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 응용 물리
  • AND-type array
  • flash memory synaptic device
  • hardware-based spiking neural network
  • neuron circuit
  • on-chip training
  • supervised-learning
  • unsupervised-learning
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
4,734 0

0.0%

' Demonstration of Hardware-based Spiking Neural Network Using an AND-type Flash Memory Array Architecture = AND-형 플래시 메모리 어레이를 활용한 하드웨어 기반 스파이킹 뉴럴 네트워크 구현' 의 참고문헌

  • `` Competitive learning : From interactive activation to adaptive resonance
    S. Grossberg vol . 11 , iss . 1 , pp . 23-63 [1987]
  • [9] M. Hu, H. Li, Y. Chen, Q. Wu, G. S. Rose, and R. W. Linderman, “Memristor Crossbar-Based Neuromorphic Computing System: A Case Study,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 10, pp. 1864- 1878, 2014.
    [2014]
  • [8] K. -H. Kim, S. Gaba, D. Wheeler, J. M. Cruz-Albrecht, T. Hussain, N. Srinivasa, and W. Lu, “A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications,” Nano Letter, vol. 12, no. 1, pp.389- 395, 2012.
    [2012]
  • [7] M. Prezioso, F. Merrikh-Bayat, B. D. Hoskins, G. C. Adam, K. K. Likharev, and D. B. Strukov, “Training and operation of an integrated neuromorphic network based on metal-oxide memristors,” Nature, vol. 521, pp. 61-64, 2015.
    [2015]
  • [6] A. Graves, A. -R. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645-6649, 2013.
    [2013]
  • [68] W. -M. Kang, D. Kwon, S. Y. Woo, S. Lee, H. Yoo, J. Kim, B. -G. Park, J. - H. Lee, “Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment,” IEEE Access, vol. 9, pp. 73121-73132, 2021.
  • [67] D. Kwon, S. Lim, J. -H. Bae, S. -T. Lee, H. Kim, C. -H. Kim, B. -G. Park, J. - H. Lee, “Adaptive weight quantization method for nonlinear synaptic devices,” IEEE Transactions on Electron Devices, vol. 66, iss. 1, pp. 395-401, 2018.
    [2018]
  • [66] S. Lim, D. Kwon, J. -H. Eum, S. -T. Lee, J. -H. Bae, H. Kim, C. –H. Kim, B. -G. Park, “Highly reliable inference system of neural networks using gated Schottky diodes,” IEEE Journal of the Electron Devices Society, vol. 7, pp. 522-528, 2019.
    [2019]
  • [65] W. -M. Kang, C. -H. Kim, S. Lee, S. Y. Woo, J. -H. Bae, B. -G. Park, J. -H. Lee, “A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices,” International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2019.
  • [64] E. O. Neftci, C. Augustine, S. Paul, and F. Detorakis, “Event-driven random back-propagation: Enabling neuromorphic deep learning machines,” Front. Neurosci., vol. 11, article 324, 2017.
    [2017]
  • [63] B. Crafton, A. Parihar, E. Gebhardt, and A. Raychowdhury, “Direct feedback alignment with sparse connections for local learning,” Front. Neurosci., vol. 13, article 525, pp. 1-12, 2019.
    [2019]
  • [61] T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman, “Random synaptic feedback weights support error backpropagation for deep learning,” Nature Comnunications, vol. 7, article 13276, pp. 1-10, 2016.
    [2016]
  • [5] K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Transactions on Neural Networks Learning Systems, vol. 28, no. 10, pp. 2222-2232, 2017.
    [2017]
  • [59] D. Querlioz, O. Bichler and C. Gamrat, "Simulation of a memristor-based spiking neural network immune to device variations," The 2011 International Joint Conference on Neural Networks, pp. 1775-1781, 2011.
    [2011]
  • [58] S. R. Kheradpisheh, M. Ganjtabesh, T. Masquelier, “Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition,” Neurocomputing, vol. 205, pp. 382-392, 2016.
    [2016]
  • [57] S. Ambrogio, N. Ciocchini, M. Laudato, V. Milo, A. Pirovano, P. Fantini, and D. Ielmini, “Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses,” Frontiers in Neuroscience, vol. 10, article 56, 2016.
    [2016]
  • [51] G. Indiveri, F. Corradi, and N. Qiao, “Neuromorphic architectures for spiking deep neural networks,” IEEE International Electron Devices Meeting (IEDM), pp. 4.2.1-4.2.4 , 2015.
    [2015]
  • [50] Y. Guo, Y. Liu, A, Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27-48, 2016.
    [2016]
  • [4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.
    [2016]
  • [49] T. N. Sainath, B. Kingsbury, G. Saon, H. Soltau, A. -R. Mohamed, G. Dahl, and B. Ramabhadran, “Deep convolutional neural networks for large-scale speech tasks,” Neural Networks, vol. 64, pp. 39-48, 2015.
    [2015]
  • [48] W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11-26, 2017.
    [2017]
  • [47] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten digit recognition with a back-propagation network,” Advances in Neural Information Processing Systems, pp. 396-404, 1989.
    [1989]
  • [46] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, “A 4-Transistors/1-Resistor hybrid synapse based on resistive switching memory (RRAM) Capable of spike-rate-dependent plasticity (SRDP),” IEEE Trans. VLSI Systems, vol. 26, iss. 12, pp. 2806-2815, 2018.
    [2018]
  • [44] C. Zamarreno-Ramos, L. A. Camunas-Mesa, J. A. Perez-Carrasco, T. Masquelier, T. Serrano-Gotarredona, and B. Linares-Barranco, “On spike-timing178 dependent plasticity, memristive devices, and building a self-learning visual cortex,” Frontiers in Neuroscience, vol. 5, article 26, pp. 1-22, 2011.
    [2011]
  • [43] V. Milo, G. Pedretti, R. Carboni, A. Calderoni, N. Ramaswamy, S. Ambrogio, and D. Ielmini, “Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity,” IEEE International Electron Devices Meeting (IEDM), pp. 440-443, 2016.
    [2016]
  • [42] G. Malavena, A. S. Spinelli, C. M. Compagnoni, “Implementing spike-timingdependent plasticity and unsupervised learning in a mainstream NOR Flash Memory Array,” IEEE International Electron Devices Meeting (IEDM), pp.2.3.1- 2.3.4, 2018.
    [2018]
  • [41] C. Diorio, P. Hasler, A. Minch, and C. A. Mead, “A single-transistor silicon synapse,” IEEE Transactions on Electron Devices, vol. 43, iss. 11, pp. 1972-1980, 1996.
    [1996]
  • [40] C. -H. Kim, S. Lee, S. Y. Woo, W. -M. Kang, S. Lim, J. -H. Bae, J. Kim, and J. -H. Lee, “Demonstration of unsupervised learning with spike-timing-dependent plasticity using a TFT-Type NOR Flash Memory Array,” IEEE Trans. Electron Devices, vol. 65, iss. 5, pp. 1774–1780, 2018.
  • [3] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinnovich, “Going deeper with convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015.
    [2015]
  • [39] C. Riggert, M. Ziegler, D. Schroeder, W. H, Krautschneider, and H. Kohlstedt, “MemFlash device: floating gate transistors as memristive devices for neuromorphic computing,” Semiconductor Science and Technology, vol. 29, no. 10, article 104011, pp. 1-9, 2014.
    [2014]
  • [38] A. Chanthbouala, V. Garcia, R. O. Cherifi, K. Bouzehouane, S. Fusil, X. Moya, S. Xavier, H. Yamada, C. Deranlot, N. D. Mathur, M. Bibes, A. Barthelemy, and J. Grollier, “A ferroelectric memristor,” Nature Materials, vol. 11, pp. 860-864, 2012.
    [2012]
  • [37] W.A. Borders, H. Akima, S. Fukami, S. Moriya, S. Kurihara, Y. Horio, S. Sato, and H. Ohno, “Analogue spin–orbit torque device for artificial-neural-network177 based associative memory operation,” Applied Physics Express, vol. 10, no. 1, article 013007, 2017.
    [2017]
  • [35] A. F. Vincent, J. Larroque, W. S. Zhao, N. B. Romdhane, O. Bichler, C. Gamrat, J. -O. Klein, S. Galdin-Retailleau, and D. Querlioz, “Spin-transfer torque magnetic memory as a stochastic memristive synapse,” IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1074-1077, 2014.
    [2014]
  • [34] S. Lequeux, J. Sampaio, V. Cros, K. Yakushiji, A. Fukushima, R. Matsumoto, H. Kubota, S. Yuasa, and J. Grollier, “A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy,” Scientific Reports, vol. 6, article 31510, 2016.
    [2016]
  • [33] S. Yu, B. Gao, Z. Fang, H. Yu, J. Kang, and H. -S. Philip Wong, “A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling,” IEEE Electron Devices Meeting, pp. 10.4.1-10.4.4, 2012.
    [2012]
  • [32] T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J. K. Gimzewski, and M. Aono, “Short-term plasticity and long-term potentiation mimicked in single inorganic synapses,” Nature Materials, vol. 10, pp. 591-595, 2011.
    [2011]
  • [31] M. Zhao, H. Wu, B. Gao, Q. Zhang, W. Wu, S. Wang, Y. Xi, D. Wu, N. Deng, S. Yu, H. Y. Chen, and H. Qian, “Investigation of statistical retention of filamentary analog RRAM for neuromorphic computing,” IEEE International Electron Devices Meeting (IEDM), pp.39.4.1-39.4.4, 2017.
    [2017]
  • [30] K. Seo, I. Kim, S. Jung, M. Jo, S. Park, J. Park, J. Shin, K. P. Biju, J. Kong, K. Lee, B. Lee, and H. Hwang, “Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device,” Nanotechnology, vol. 22, no. 25, article 254023, 2011.
  • [2] T. N. Sainath, B. Kingsbury, G. Saon, H. Soltau, A. -R. Mohamed, G. Dahl, and B. Ramabhadran, “Deep Convolutional Neural Networks for Large-scale Speech Tasks,” Neural Networks, vol. 64, pp. 39-48, 2015.
    [2015]
  • [29] D. Kuzum, R. G. D. Jeyasingh, B. Lee, and H. -S. Philip Wong, “Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-inspired Computing,” Nano letters, vol. 12, no. 5, pp. 2179-2186, 2012.
    [2012]
  • [28] S. B. Eryilmaz, D. Kuzum, G. D. Jeyasingh, S. B. Kim, M. BrightSky, C. Lam, and H. -S. Philip Wong, “Experimental demonstration of array-level learning with phase change synaptic devices,” IEEE International Electron Devices Meeting (IEDM), pp. 25.5.1-25.5.4, 2013.
    [2013]
  • [27] M. Suri, O. Bichler, D. Querlioz, O. Cueto, L. Perniola, V. Sousa, D. Vuillaume, C. Gamrat, and B. DeSalvo, “Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction,” IEEE Electron Devices Meeting, pp. 4.4.1-4.4.4, 2011.
    [2011]
  • [26] T. Soliman et al., "Ultra-Low Power Flexible Precision FeFET Based Analog In-Memory Computing," 2020 IEEE International Electron Devices Meeting (IEDM), pp. 29.2.1-29.2.4, 2020.
    [2020]
  • [25] X. Zhang et al., "Fully Memristive SNNs with Temporal Coding for Fast and Low-power Edge Computing," 2020 IEEE International Electron Devices Meeting (IEDM), pp. 29.6.1-29.6.4, 2020.
    [2020]
  • [24] S. Lee, H. Kim, J. Bae, H. Yoo, N. Y. Choi, D. Kwon, S. Lim, B. -G. Park, and J. -H. Lee, "High-Density and Highly-Reliable Binary Neural Networks Using NAND Flash Memory Cells as Synaptic Devices," 2019 IEEE International Electron Devices Meeting (IEDM), pp. 38.4.1-38.4.4, 2019.
    [2019]
  • [23] G. Malavena, A. S. Spinelli, and C. M. Compagnoni, “Implementing Spike175 Timing-Dependent Plasticity and Unsupervised Learning in a Mainstream NOR Flash Memory Array,” 2018 IEEE International Electron Devices Meeting (IEDM), pp. 2.3.1-2.3.4, 2018.
    [2018]
  • [22] M. Prezioso, F. Merrikh-Bayat, B. D. Hoskins, G. C. Adam, K. K. Likharev, and D. B. Strukov, “Training and operation of an integrated neuromorphic network based on metal-oxide memristors,” Nature, vol. 521, pp. 61-64, 2015.
    [2015]
  • [21] K. -H. Kim, S. Gaba, D. Wheeler, J. M. Cruz-Albrecht, T. Hussain, N. Srinivasa, and W. Lu, “A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications,” Nano Letter, vol. 12, no. 1, pp. 389-395, 2011.
    [2011]
  • [20] M. Hu, H. Li, Y. Chen, Q. Wu, G. S. Rose, and R. W. Linderman, “Memristor crossbar-based neuromorphic computing system: A case study,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, issue 10, pp. 1864-1878, 2014.
    [2014]
  • [17] N. Qiao, H. Mostafa, F. Corradi, M. Osswald, F. Stefanini, D. Sumislawska, and G. Indiveri, “A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses,” Frontiers in Neuroscience, vol. 9. article 141. 2015.
    [2015]
  • [14] A. Basu, S. Shuo, H. Zhou, M. H. Lim, G. -B. Huang, “Silicon spiking neurons for hardware implementation of extreme learning machines,” Neurocomputing, vol. 102, pp. 125-134, 2012.
    [2012]
  • [13] 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, issue 3, pp. 288-295, 2013.
    [2013]
  • [12] C. -C. Chang, P. -C. Chen, T. Chou, I. -T. Wang, B. Hudec, C. -C. Chang, C. -M. Tsai, T. -S. Chang, and T. -H. Hou, “Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network Based on Analog Resistive Synapse,” IEEE Journal of Emerging Selected Topics in Circuits and Systems, vol. 8, no. 1, pp. 116-124, 2018.
  • [11] S. Lim, J. -H. Bae, J. -H. Eum, S. Lee, C. -H. Kim, D. Kwon, B. -G. Park, and J. -H. Lee, “Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices,” Neural Computing and Applications, vol. 31, pp. 8101–8116, 2018.
    [2018]
  • [10] E. O. Neftci, C. Augustine, S. Paul, and G. Detorakis, “Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines,” Frontiers in Neuroscience, vol. 11, article 324, 2017.
    [2017]
  • Unsupervised learning of digit recognition using spike-timing-dependent plasticity
    P. U. Diehl , and M. Cook , vol . 9 , article 99 , pp . 1-9 [2015]
  • Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
    T. Masquelier , and S. J. Thorpe vol . 3 , no . 2 , pp . 247 ? 257 [2007]
  • Training deep spiking neural networks using backpropagation.
    J. H. Le , T. Delbruck , and M. Pfeiffer , vol . 10 , article 508 [2016]
  • Training deep spiking neural networks using backpropagation ,
    J. H. Lee , T. Delbruck , and M. Pfeiffer , vol . 10 , article 508 , pp . 1-13 [2016]
  • The neurobiological significance of the new learning models
    D. Zipser and D. E. Rumelhart pp . 192-200 [1993]
  • Spatio-temporal backpropagation for training high-performance spiking neural networks
  • NeuCube : A spiking neural network architecture for mapping , learning and understanding of spatio-temporal brain data
    N. K. Kasabov vol . 52 , pp . 62-76 , [2014]
  • Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On- Chip STDP Learning
    G. Srinivasan , A. Sengupta , and K. Roy , vol . 6 , article 29545 [2016]
  • Imagenet classification with deep convolutional neural networks
  • Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition ,
    X. Wu , V. Saxena , and K. Zhu vol.5 , iss , 2 , pp . 254-266 [2015]
  • H Supervised learning based on temporal coding in spiking neural networks
    H. Mostafa vol . 29 , issue 7 , pp . 3227 ? 3235 [2016]
  • Direct feedback alignment provides learning in deep neural networks
    A. Nokland 29 , pp . 1037-1045 [2016]
  • A compound memristive synapse model for statistical learning through STDP in spiking neural networks
    J . Bill and R. Legenstein , vol . 8 , article 412 , [2014]