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Alexandre Levisse et al.
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Avishek Biswas et al.
IEEE JOURNAL OF SOLID-STATE CIRCUITS (2019)
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Feichi Zhou et al.
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Fuxi Cai et al.
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Kea-Tiong Tang et al.
2019 SYMPOSIUM ON VLSI CIRCUITS (2019)
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Equivalent-accuracy accelerated neural-network training using analogue memory
Stefano Ambrogio et al.
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Neuromorphic computing with multi-memristive synapses
Irem Boybat et al.
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Artificial optic-neural synapse for colored and color-mixed pattern recognition
Seunghwan Seo et al.
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Fully memristive neural networks for pattern classification with unsupervised learning
Zhongrui Wang et al.
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T. Abbey et al.
2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) (2018)
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Daniele Ielmini et al.
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Mixed-precision in-memory computing
Manuel Le Gallo et al.
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Mohammed A. Zidan et al.
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Analogue signal and image processing with large memristor crossbars
Can Li et al.
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Max M. Shulaker et al.
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Sparse coding with memristor networks
Patrick M. Sheridan et al.
NATURE NANOTECHNOLOGY (2017)
Face classification using electronic synapses
Peng Yao et al.
NATURE COMMUNICATIONS (2017)
Training andoperation of an integrated neuromorphic network based on metal-oxide memristors
M. Prezioso et al.
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H. -S. Philip Wong et al.
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Logic Computation in Phase Change Materials by Threshold and Memory Switching
M. Cassinerio et al.
ADVANCED MATERIALS (2013)
Memristive devices for computing
J. Joshua Yang et al.
NATURE NANOTECHNOLOGY (2013)
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Li Deng
IEEE SIGNAL PROCESSING MAGAZINE (2012)
'Memristive' switches enable 'stateful' logic operations via material implication
Julien Borghetti et al.
NATURE (2010)
Programmable computing with a single magnetoresistive element
A Ney et al.
NATURE (2003)