Spiking neural network circuit with op-amp based LIF neuron and RRAM synaptic array
Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier GmbH
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120579/1/120579.pdf http://psasir.upm.edu.my/id/eprint/120579/ https://linkinghub.elsevier.com/retrieve/pii/S1434841125003450 |
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| Summary: | Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability for neuromorphic computing applications. In this article, an SNN circuit with a 1T-1R RRAM synaptic array along with op-amp and 555 timer-based leaky integrate-and-fire (LIF) neuron is implemented to use for pattern recognition. The input pre-spikes from the pattern are applied to the RRAM synaptic array, which exhibits synaptic plasticity. The LIF neuron processes the synaptic array output to produce post-spikes, which modify the conductance of the RRAM synaptic array based on the spike-timing-dependent plasticity (STDP) mechanism. The unique output spikes obtained for different characters can be used for pattern recognition of the characters. |
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