Signal and noise extraction from analog memory elements for neuromorphic computing
暂无分享,去创建一个
V. Narayanan | A. Sebastian | T. Ando | S. Kim | I. Boybat | N. Gong | T. Idé
[1] Steven J. Plimpton,et al. Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator , 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[2] Shimeng Yu,et al. Improving Analog Switching in HfOx-Based Resistive Memory With a Thermal Enhanced Layer , 2017, IEEE Electron Device Letters.
[3] Yusuf Leblebici,et al. Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks , 2017, 2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME).
[4] Thomas P. Parnell,et al. Temporal correlation detection using computational phase-change memory , 2017, Nature Communications.
[5] H.-S. Philip Wong,et al. Face classification using electronic synapses , 2017, Nature Communications.
[6] Heiner Giefers,et al. Mixed-precision in-memory computing , 2017, Nature Electronics.
[7] Pritish Narayanan,et al. Neuromorphic computing using non-volatile memory , 2017 .
[8] Wei D. Lu,et al. Sparse coding with memristor networks. , 2017, Nature nanotechnology.
[9] Evangelos Eleftheriou,et al. Inherent stochasticity in phase-change memory devices , 2016, 2016 46th European Solid-State Device Research Conference (ESSDERC).
[10] I-Ting Wang,et al. 3D Ta/TaOx/TiO2/Ti synaptic array and linearity tuning of weight update for hardware neural network applications , 2016, Nanotechnology.
[11] Manuel Le Gallo,et al. Stochastic phase-change neurons. , 2016, Nature nanotechnology.
[12] Steven J. Plimpton,et al. Resistive memory device requirements for a neural algorithm accelerator , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[13] H. Hwang,et al. Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems , 2016, IEEE Electron Device Letters.
[14] Runchen Fang,et al. A CMOS-compatible electronic synapse device based on Cu/SiO2/W programmable metallization cells , 2016, Nanotechnology.
[15] Gökmen Tayfun,et al. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations , 2016, Front. Neurosci..
[16] R. Jordan,et al. NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).
[17] Shimeng Yu,et al. Mitigating effects of non-ideal synaptic device characteristics for on-chip learning , 2015, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[18] Abu Sebastian,et al. Accumulation-Based Computing Using Phase-Change Memories With FET Access Devices , 2015, IEEE Electron Device Letters.
[19] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[20] Yoon-Ha Jeong,et al. Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems , 2015, IEEE Electron Device Letters.
[21] Fabien Alibart,et al. Plasticity in memristive devices for spiking neural networks , 2015, Front. Neurosci..
[22] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[23] 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.
[24] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.
[25] Daniela M. Witten,et al. An Introduction to Statistical Learning: with Applications in R , 2013 .
[26] C. Wright,et al. Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices , 2013 .
[27] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[28] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.
[29] E. Miranda,et al. The Quantum Point-Contact Memristor , 2012, IEEE Electron Device Letters.
[30] Byoungil Lee,et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.
[31] D. Ielmini,et al. Modeling the Universal Set/Reset Characteristics of Bipolar RRAM by Field- and Temperature-Driven Filament Growth , 2011, IEEE Transactions on Electron Devices.
[32] C. Hagleitner,et al. Device, circuit and system-level analysis of noise in multi-bit phase-change memory , 2010, 2010 International Electron Devices Meeting.
[33] Wei Yang Lu,et al. Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.
[34] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[35] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[36] M. Breitwisch. Phase Change Memory , 2008, 2008 International Interconnect Technology Conference.
[37] M. Breitwisch,et al. Novel Lithography-Independent Pore Phase Change Memory , 2007, 2007 IEEE Symposium on VLSI Technology.