A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation

By Shimeng Yu, B. Gao, Zheng-yu Fang, Hongyu Yu, Jinfeng Kang, H. P. Wong. Advances in Materials, 2013

495 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7f1586dc87e292614cd32e1efb8fe52a18b31c1e

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In Electrical and Electronic Engineering

Citations in the corpus

Fully memristive neural networks for pattern classification with unsupervised learning (2018, background): “Lateral inhibition is deployed, which is typical in fully connected feedforward networks to enhance the discrimination of the inputs and make the self-adapting network energy efficient 12,47–50 .”
Face classification using electronic synapses (2017, background): “RRAM devices based on resistive switching phenomenon exhibit promising potential as the electronic synapse 26–29 .”
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations (2016, methodology): “We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology.”
Artificial synapse network on inorganic proton conductor for neuromorphic systems (2013, methodology): “[32] The energy dissipation of single spike event is estimated to be ~45 pJ, which is lower than that of the artificial synapse based on conventional CMOS circuit [33] and is comparable to the energy dissipation of the reported hard-ware based artificial synapse [8, 9, 15] At present, the channel wi”
In-memory computing with resistive switching devices (2018, methodology): “Cumulative change of resistance has been adopted for analog synapses in artificial neural networks using both filamentary [67-69] and interface-switching RRAM devices [70], as well as ferroelectric devices [71] and domain-wall STT-MRAM devices [72].”
Memory and Information Processing in Neuromorphic Systems (2015, background): “In particular, there have been many recent publications describing the use of new materials and nano-technologies for building nano-scale devices that can emulate some of the properties observed in biological synapses [15], [16], [17], [18], [19], [20].”