Analog Implementation of a Spiking Neuron with Memristive Synapses for Deep Learning Processing

Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-07, Vol.12 (13), p.2025
Main Authors: Ramirez-Morales, Royce R., Ponce-Ponce, Victor H., Molina-Lozano, Herón, Sossa-Azuela, Humberto, Islas-García, Oscar, Rubio-Espino, Elsa
Format: Article
Language:English
Subjects:
Algorithms
Analog circuits
Artificial intelligence
Artificial neural networks
Biological effects
Brain
Circuit boards
CMOS
Composite materials
Deep learning
Energy consumption
Graphics processing units
Machine learning
Memory devices
memristor
neuromorphic
Neuromorphic computing
Neurons
Printed circuits
Prototypes
Prototyping
SNN
Spiking
STDP
Synapses
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Summary:Analog neuromorphic prototyping is essential for designing and testing spiking neuron models that use memristive devices as synapses. These prototypes can have various circuit configurations, implying different response behaviors that custom silicon designs lack. The prototype’s behavior results can be optimized for a specific foundry node, which can be used to produce a customized on-chip parallel deep neural network. Spiking neurons mimic how the biological neurons in the brain communicate through electrical potentials. Doing so enables more powerful and efficient functionality than traditional artificial neural networks that run on von Neumann computers or graphic processing unit-based platforms. Therefore, on-chip parallel deep neural network technology can accelerate deep learning processing, aiming to exploit the brain’s unique features of asynchronous and event-driven processing by leveraging the neuromorphic hardware’s inherent parallelism and analog computation capabilities. This paper presents the design and implementation of a leaky integrate-and-fire (LIF) neuron prototype implemented with commercially available components on a PCB board. The simulations conducted in LTSpice agree well with the electrical test measurements. The results demonstrate that this design can be used to interconnect many boards to build layers of physical spiking neurons, with spike-timing-dependent plasticity as the primary learning algorithm, contributing to the realization of experiments in the early stage of adopting analog neuromorphic computing.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12132025