CMOS compatible building blocks

This project explores the application of physical reservoir computing in predicting alcohol sales and highlights its potential to match the recurrent neural networks in performance with less complexity and improve efficiency when dealing with sequential data taskings. The project further explores th...

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Main Author: Tan, Darel Teng Kiat
Other Authors: Ang Diing Shenp
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176832
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1768322024-05-31T15:42:36Z CMOS compatible building blocks Tan, Darel Teng Kiat Ang Diing Shenp School of Electrical and Electronic Engineering EDSAng@ntu.edu.sg Engineering This project explores the application of physical reservoir computing in predicting alcohol sales and highlights its potential to match the recurrent neural networks in performance with less complexity and improve efficiency when dealing with sequential data taskings. The project further explores the fine-tuning of the transistor model parameters and demonstrates how a physical system with non-linearity can be harnessed to reduce training load and latency. PRC models leverage the dynamics of a physical system such as transistor model to enable diverse applications for a rapid and energy-efficient output. The results demonstrate that PRC delivers better results to RNN for sequential data tasking and reveals the potential of transistor base model as an alternative to RNN while enhancing computational efficiency and speed. This is a prospective impact on advancing artificial intelligence and machine learning models. Bachelor's degree 2024-05-27T05:37:23Z 2024-05-27T05:37:23Z 2024 Final Year Project (FYP) Tan, D. T. K. (2024). CMOS compatible building blocks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176832 https://hdl.handle.net/10356/176832 en A2014-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Tan, Darel Teng Kiat
CMOS compatible building blocks
description This project explores the application of physical reservoir computing in predicting alcohol sales and highlights its potential to match the recurrent neural networks in performance with less complexity and improve efficiency when dealing with sequential data taskings. The project further explores the fine-tuning of the transistor model parameters and demonstrates how a physical system with non-linearity can be harnessed to reduce training load and latency. PRC models leverage the dynamics of a physical system such as transistor model to enable diverse applications for a rapid and energy-efficient output. The results demonstrate that PRC delivers better results to RNN for sequential data tasking and reveals the potential of transistor base model as an alternative to RNN while enhancing computational efficiency and speed. This is a prospective impact on advancing artificial intelligence and machine learning models.
author2 Ang Diing Shenp
author_facet Ang Diing Shenp
Tan, Darel Teng Kiat
format Final Year Project
author Tan, Darel Teng Kiat
author_sort Tan, Darel Teng Kiat
title CMOS compatible building blocks
title_short CMOS compatible building blocks
title_full CMOS compatible building blocks
title_fullStr CMOS compatible building blocks
title_full_unstemmed CMOS compatible building blocks
title_sort cmos compatible building blocks
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176832
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