2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses

Pande, Shubham; Srinivasan, Karthi; Balanethiram, Suresh; Chakrabarti, Bhaswar; Chakravorty, Anjan

doi:10.1109/icecs202256217.2022.9970889

Cited by 1 publication

(2 citation statements)

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“…In ref. [139], graphene–insulator–graphene memristive devices were implemented as synapses to perform 2D motion detection. A physics‐based, SPICE‐compatible model was used to emulate the synaptic device, which had previously been characterized in ref.…”

Section: Graphene Memristive Neuromorphic Networkmentioning

confidence: 99%

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A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits

Walters

Jacob

Amirsoleimani

et al. 2023

Advanced Intelligent Systems

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As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's low‐power and high‐speed computations, making it crucial in the era of big data and artificial intelligence. One significant development in this field is the memristor, a device that exhibits neuromorphic tendencies. The performance of memristive devices and circuits relies on the materials used, with graphene being a promising candidate due to its unique properties. Researchers are investigating graphene‐based memristors for large‐scale, sustainable fabrication. Herein, progress in the development of graphene‐based memristive neuromorphic devices and circuits is highlighted. Graphene and its common fabrication methods are discussed. The fabrication and production of graphene‐based memristive devices are reviewed and comparisons are provided among graphene‐ and nongraphene‐based memristive devices. Next, a detailed synthesis of the devices utilizing graphene‐based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs). Finally, insights on the prospects of graphene‐based neuromorphic memristive systems including their device‐ and network‐level challenges and opportunities are given.

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Section: Graphene Memristive Neuromorphic Networkmentioning

confidence: 99%

“…As such, testing graphene‐based devices in networks performing tasks well suited for neuromorphic platforms is another critical area of research. [ 138,139 ] In ref. [138], two graphene excitable lasers were connected in cascade to perform a simple coincidence detection task.…”

Section: Graphene Memristive Neuromorphic Networkmentioning

confidence: 99%