Analogue-Domain Machine Learning Computations Exploiting 2D Materials Properties
type: CreativeWork
description: For several decades, digital complimentary metal-oxide-semiconductor (CMOS) has been the dominant technology in computer hardware. This is a result of its abundance, as well as material properties, including stable native oxide formation, and ability to be miniaturised. However, new application classes and the physical limitations of traditional digital CMOS have triggered a wave of research into alternative materials and devices for computing applications. Analysing and identifying trends is vital for guiding new ideas and research. This thesis establishes the context of the work by first exploring trends in integrated circuit (IC) development. This thesis shows that the rapid growth of machine learning (ML) has resulted in a significant increase in the number of ML accelerators. However, many of these accelerators rely on conventional CMOS technology and so are subject to the same issues of scalability, a limitation known as the *accelerator wall*. Thus, new materials and devices are of interest. The thesis then discusses how Graphene and related materials (GRMs) in particular may help overcome this wall and facilitate continued progress in the development of computer hardware. To establish the state of the art, this thesis examines several candidate technologies for future computer devices. Despite an intense research focus in recent years, very few novel devices and materials for computation have seen large-scale commercialisation. This thesis explores the reasons for this and examines successful examples of novel devices and materials being applied as solutions to computational problems. This demonstrates that early-stage mapping of computational problems to devices and materials properties has the potential for a significant impact, providing a guiding principle for the subsequent work in the thesis. The thesis then investigates graphene field-effect transistors (GFETs), a type of field-effect transistor with a channel made of graphene, for computational appli…
abstract: For several decades, digital complimentary metal-oxide-semiconductor (CMOS) has been the dominant technology in computer hardware. This is a result of its abundance, as well as material properties, including stable native oxide formation, and ability to be miniaturised. However, new application classes and the physical limitations of traditional digital CMOS have triggered a wave of research into alternative materials and devices for computing applications. Analysing and identifying trends is vital for guiding new ideas and research. This thesis establishes the context of the work by first exploring trends in integrated circuit (IC) development. This thesis shows that the rapid growth of machine learning (ML) has resulted in a significant increase in the number of ML accelerators. However, many of these accelerators rely on conventional CMOS technology and so are subject to the same issues of scalability, a limitation known as the *accelerator wall*. Thus, new materials and devices are of interest. The thesis then discusses how Graphene and related materials (GRMs) in particular may help overcome this wall and facilitate continued progress in the development of computer hardware. To establish the state of the art, this thesis examines several candidate technologies for future computer devices. Despite an intense research focus in recent years, very few novel devices and materials for computation have seen large-scale commercialisation. This thesis explores the reasons for this and examines successful examples of novel devices and materials being applied as solutions to computational problems. This demonstrates that early-stage mapping of computational problems to devices and materials properties has the potential for a significant impact, providing a guiding principle for the subsequent work in the thesis. The thesis then investigates graphene field-effect transistors (GFETs), a type of field-effect transistor with a channel made of graphene, for computational appli…
author: Tye, Nathaniel
language: eng
keywords: 2D Materials, Graphene, Machine learning, Random Number
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/e0710dc4-37dc-4f44-937c-4638f53c66d1/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2022-09-20
date modified: 2026-07-22T22:24:03Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.98897