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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 |
Appears in Collections: | Elements Staff Publications |
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