Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICICDT.2019.8790886
Title: Design of Artificial Spiking Neuron with SiO2 Memristive Synapse to Demonstrate Neuron-Level Spike Timing Dependent Plasticity
Authors: NIU XUHUA 
LI YIDA 
UMESH CHAND 
LEONG JIN FENG 
EVGENY ZAMBURG 
THEAN VOON YEW, AARON 
Keywords: Co-Optimization; Spiking Neuron Network; Resistive Random Access Memory; Spike Timing Dependent Plasticity; Long-Term Potentiation; Long-Term Depression
Issue Date: 8-Aug-2019
Publisher: IEEE
Citation: NIU XUHUA, LI YIDA, UMESH CHAND, LEONG JIN FENG, EVGENY ZAMBURG, THEAN VOON YEW, AARON (2019-08-08). Design of Artificial Spiking Neuron with SiO2 Memristive Synapse to Demonstrate Neuron-Level Spike Timing Dependent Plasticity. IEEE International Conference on IC Design and Technology (ICICDT). ScholarBank@NUS Repository. https://doi.org/10.1109/ICICDT.2019.8790886
Rights: CC0 1.0 Universal
Abstract: In this paper, we report on the Design-Technology Co-optimization (DTCO) of an artificial spiking neuron utilizing a leaky-integrated-and-fire (LIF) circuit with a SiO 2 based sub-1.2V analog resistive random-access memory (ReRAM). The memristor integrated artificial neuron is capable of spike-timing-dependent plasticity (STDP), necessary for the implementation of Spiking Neural Networks (SNNs). In addition, for the first time, we designed and demonstrated a STDP circuit in conjunction with the SiO 2 -based ReRAM that is capable of generating forward- and back-propagation signals. This allows for self-modulation of the synaptic weights through the Long-term potentiation (LTP) and Long-term depression (LTD) effect.
Source Title: IEEE International Conference on IC Design and Technology (ICICDT)
URI: https://scholarbank.nus.edu.sg/handle/10635/192009
DOI: 10.1109/ICICDT.2019.8790886
Rights: CC0 1.0 Universal
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