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Title: A Spiking Neuromorphic Architecture Using Gated-RRAM for Associative Memory
This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework.
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Award ID(s):
1926465 1718428
Publication Date:
Journal Name:
ACM Journal on Emerging Technologies in Computing Systems
Page Range or eLocation-ID:
1 to 22
Sponsoring Org:
National Science Foundation
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