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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176832 |
| _version_ | 1824455841375322112 |
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| author | Tan, Darel Teng Kiat |
| author2 | Ang Diing Shenp |
| author_facet | Ang Diing Shenp Tan, Darel Teng Kiat |
| author_sort | Tan, Darel Teng Kiat |
| collection | NTU |
| 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. |
| first_indexed | 2025-02-19T03:44:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176832 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:44:37Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering Tan, Darel Teng Kiat CMOS compatible building blocks |
| title | CMOS compatible building blocks |
| title_full | CMOS compatible building blocks |
| title_fullStr | CMOS compatible building blocks |
| title_full_unstemmed | CMOS compatible building blocks |
| title_short | CMOS compatible building blocks |
| title_sort | cmos compatible building blocks |
| topic | Engineering |
| url | https://hdl.handle.net/10356/176832 |
| work_keys_str_mv | AT tandareltengkiat cmoscompatiblebuildingblocks |