Training Neural Networks using Memristive Devices with Nonlinear Accumulative Behavior
暂无分享,去创建一个
Manuel Le Gallo | Abu Sebastian | Christophe Piveteau | Riduan Khaddam-Aljameh | A. Sebastian | M. Le Gallo | R. Khaddam-Aljameh | C. Piveteau
[1] Tayfun Gokmen,et al. Training LSTM Networks With Resistive Cross-Point Devices , 2018, Front. Neurosci..
[2] Abu Sebastian,et al. Tutorial: Brain-inspired computing using phase-change memory devices , 2018, Journal of Applied Physics.
[3] E. Eleftheriou,et al. A phase-change memory model for neuromorphic computing , 2018, Journal of Applied Physics.
[4] Shahram Minaei,et al. Applications of a CMOS current squaring circuit in analog signal processing , 2015, 2015 38th International Conference on Telecommunications and Signal Processing (TSP).
[5] Evangelos Eleftheriou,et al. Mixed-precision architecture based on computational memory for training deep neural networks , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).
[6] Shimeng Yu,et al. Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.
[7] M. Breitwisch,et al. Novel Lithography-Independent Pore Phase Change Memory , 2007, 2007 IEEE Symposium on VLSI Technology.
[8] Heiner Giefers,et al. Compressed Sensing With Approximate Message Passing Using In-Memory Computing , 2018, IEEE Transactions on Electron Devices.
[9] Catherine E. Graves,et al. Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine , 2018, Advanced materials.
[10] Wei D. Lu,et al. Sparse coding with memristor networks. , 2017, Nature nanotechnology.
[11] Pritish Narayanan,et al. Equivalent-accuracy accelerated neural-network training using analogue memory , 2018, Nature.
[12] Pritish Narayanan,et al. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element , 2014, IEEE Transactions on Electron Devices.
[13] Damien Querlioz,et al. Narrow Heater Bottom Electrode‐Based Phase Change Memory as a Bidirectional Artificial Synapse , 2018, Advanced Electronic Materials.
[14] Abu Sebastian,et al. Accumulation-Based Computing Using Phase-Change Memories With FET Access Devices , 2015, IEEE Electron Device Letters.
[15] Gökmen Tayfun,et al. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations , 2016, Front. Neurosci..
[16] G. W. Burr,et al. Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.
[17] Heiner Giefers,et al. Mixed-precision in-memory computing , 2017, Nature Electronics.
[18] H.-S. Philip Wong,et al. In-memory computing with resistive switching devices , 2018, Nature Electronics.
[19] Thomas P. Parnell,et al. Temporal correlation detection using computational phase-change memory , 2017, Nature Communications.
[20] N Gong,et al. Signal and noise extraction from analog memory elements for neuromorphic computing , 2018, Nature Communications.
[21] Daniel Krebs,et al. Crystal growth within a phase change memory cell , 2014, Nature Communications.
[22] Manuel Le Gallo,et al. Stochastic phase-change neurons. , 2016, Nature nanotechnology.
[23] Yusuf Leblebici,et al. Neuromorphic computing with multi-memristive synapses , 2017, Nature Communications.
[24] Hyunsang Hwang,et al. Improved Synaptic Behavior of CBRAM Using Internal Voltage Divider for Neuromorphic Systems , 2018, IEEE Transactions on Electron Devices.
[25] Pritish Narayanan,et al. Neuromorphic computing using non-volatile memory , 2017 .
[26] Tayfun Gokmen,et al. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices , 2017, Front. Neurosci..
[27] Evangelos Eleftheriou,et al. Inherent stochasticity in phase-change memory devices , 2016, 2016 46th European Solid-State Device Research Conference (ESSDERC).