Design Space Evaluation of a Memristor Crossbar Based Multilayer Perceptron for Image Processing

Yakopcic, Chris; Fernando, B. Rasitha; Taha, Tarek M.

doi:10.1109/ijcnn.2019.8852005

Cited by 2 publications

(1 citation statement)

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“…Starzyk et al ( 2014 ) developed a novel neural network architecture that utilizes a compact crossbar layout of memristors, which allows us to preserve a high density of synaptic connections. Yakopcic et al ( 2019 ) studied a memristor-based neuromorphic system for ex-situ training of multi-layer perceptron algorithms. This technique facilitates the direct translation of neural algorithm weights onto the resistive grid of a memristor crossbar.…”

Section: Neuro-memristive Computingmentioning

confidence: 99%

Graphene-based RRAM devices for neural computing

Das,

Reghuvaran

et al. 2023

Front. Neurosci.

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Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.

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Section: Neuro-memristive Computingmentioning

confidence: 99%