Modeling and Analysis of CMOS-based Folded Memristive Crossbar Array for 3D Neuromorphic Integrated Circuits

Thomas, Sherin Ann; Vohra, Sahibia Kaur; Kushwaha, Suyash; Sharma, Rohit; Das, Devarshi Mrinal

doi:10.1109/ectc51909.2023.00164

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2024

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Cited by 2 publications

References 23 publications

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Modeling and Comparing the Impact of Resistive and Capacitive Crossbar Associated Parasitics of Neuromorphic Circuits

Thomas,

Kushwaha,

Sharma

et al. 2024

2024 31st International Conference on Mixed Design of Integrated Circuits and System (MIXDES)

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Modeling and Comparing the Impact of Resistive and Capacitive Crossbar Associated Parasitics of Neuromorphic Circuits

Thomas,

Kushwaha,

Sharma

et al. 2024

2024 31st International Conference on Mixed Design of Integrated Circuits and System (MIXDES)

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PySpice-Simulated In Situ Learning with Memristor Emulation for Single-Layer Spiking Neural Networks

Jurj

2024

Electronics

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This paper presents a novel approach to in situ memristive learning by training spiking neural networks (SNNs) entirely within the circuit using memristor emulators in SPICE. The circuit models neurons using Lapicque neurons and employs pulse-based spike encoding to simulate spike-timing-dependent plasticity (STDP), a key learning mechanism in SNNs. The Lapicque neuron model operates according to the Leaky Integrate-and-Fire (LIF) model, which is used in this study to model spiking behavior in memristor-based SNNs. More exactly, the first memristor emulator in PySpice, a Python library for circuit simulation, was developed and integrated into a memristive circuit capable of in situ learning, named the “In Situ Memristive Learning Method for Pattern Classification.” This novel technique enables time-based computation, where neurons accumulate incoming spikes and fire once a threshold is reached, mimicking biological neuron behavior. The proposed method was rigorously tested on three diverse datasets: XPUE, a custom non-dominating 3 × 3 image dataset; a 3 × 5 digit dataset ranging from 0 to 5; and a resized 10 × 10 version of the Modified National Institute of Standards and Technology (MNIST) dataset. The neuromorphic circuit achieved successful pattern learning across all three datasets, outperforming comparable results from other in situ training simulations on SPICE. The learning process harnesses the cumulative effect of memristors, enabling the network to learn a representative pattern for each label efficiently. This advancement opens new avenues for neuromorphic computing and paves the way for developing autonomous, adaptable pattern classification neuromorphic circuits.

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Modeling and Analysis of CMOS-based Folded Memristive Crossbar Array for 3D Neuromorphic Integrated Circuits

Cited by 2 publications

References 23 publications

Modeling and Comparing the Impact of Resistive and Capacitive Crossbar Associated Parasitics of Neuromorphic Circuits

Modeling and Comparing the Impact of Resistive and Capacitive Crossbar Associated Parasitics of Neuromorphic Circuits

PySpice-Simulated In Situ Learning with Memristor Emulation for Single-Layer Spiking Neural Networks

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