Generalized Convolution Simulation Stack for RRAM Device based Deep Learning Neural Network
暂无分享,去创建一个
[1] Rolf Drechsler,et al. Logic Synthesis for RRAM-Based In-Memory Computing , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[2] Gregory S. Snider,et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication , 2010, Nature.
[3] Jefersson Alex dos Santos,et al. Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..
[4] H.-S. Philip Wong,et al. In-memory computing with resistive switching devices , 2018, Nature Electronics.
[5] Lei Xu,et al. 16 Boolean logics in three steps with two anti-serially connected memristors , 2015 .
[6] J.F. Kang,et al. RRAM based convolutional neural networks for high accuracy pattern recognition and online learning tasks , 2017, 2017 Silicon Nanoelectronics Workshop (SNW).
[7] L. Goux,et al. Intrinsic switching variability in HfO2 RRAM , 2013, 2013 5th IEEE International Memory Workshop.
[8] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[9] Tsuyoshi Murata,et al. {m , 1934, ACML.
[10] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Sparsh Mittal,et al. A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks , 2018, Mach. Learn. Knowl. Extr..