Hardware-friendly neural network design and optimization for low power IOT applications
As speech becomes a popular way for human to interact with electronic devices in recent years, it leads to interests to apply machine learning in speech related applications, such as sound classification, speech recognition and so on. One of the exciting applications is to develop keyword spottin...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157996 |
| _version_ | 1826129952684113920 |
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| author | Wang, Yingfeng |
| author2 | Goh Wang Ling |
| author_facet | Goh Wang Ling Wang, Yingfeng |
| author_sort | Wang, Yingfeng |
| collection | NTU |
| description | As speech becomes a popular way for human to interact with electronic devices in recent years, it leads to interests to apply machine learning in speech related applications, such as sound classification, speech recognition and so on.
One of the exciting applications is to develop keyword spotting (KWS) module using neural network. The KWS module is acting as a switch to activate a downstream system, for example, a speech recognition system after certain keywords have been detected. In actual application, a high accuracy KWS which is able to identify the keyword with the existence of background noises is desired for a smooth user experience.
Thus, this project aims to design a hardware-friendly and noise-robust neural network for KWS, expecting to classify 10 keywords along with “silence” and “unknown” class. A final LSTM model with 4-bit quantization and k=9 pruning shows 91.74% accuracy on clean audio, with a model size of 7.9KB. Compared to other state-of- the-art KWS architectures classifying the same number of keywords, this work is able to achieve a 2-4% higher accuracy for both clean and noisy audios, as well as a size reduction of at least 29%. |
| first_indexed | 2024-10-01T07:48:56Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157996 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:48:56Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1579962023-07-07T19:13:34Z Hardware-friendly neural network design and optimization for low power IOT applications Wang, Yingfeng Goh Wang Ling School of Electrical and Electronic Engineering A*STAR Institute of Microelectronics Do Anh Tuan EWLGOH@ntu.edu.sg, doat@ime.a-star.edu.sg Engineering::Electrical and electronic engineering As speech becomes a popular way for human to interact with electronic devices in recent years, it leads to interests to apply machine learning in speech related applications, such as sound classification, speech recognition and so on. One of the exciting applications is to develop keyword spotting (KWS) module using neural network. The KWS module is acting as a switch to activate a downstream system, for example, a speech recognition system after certain keywords have been detected. In actual application, a high accuracy KWS which is able to identify the keyword with the existence of background noises is desired for a smooth user experience. Thus, this project aims to design a hardware-friendly and noise-robust neural network for KWS, expecting to classify 10 keywords along with “silence” and “unknown” class. A final LSTM model with 4-bit quantization and k=9 pruning shows 91.74% accuracy on clean audio, with a model size of 7.9KB. Compared to other state-of- the-art KWS architectures classifying the same number of keywords, this work is able to achieve a 2-4% higher accuracy for both clean and noisy audios, as well as a size reduction of at least 29%. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-26T12:53:16Z 2022-05-26T12:53:16Z 2022 Final Year Project (FYP) Wang, Y. (2022). Hardware-friendly neural network design and optimization for low power IOT applications. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157996 https://hdl.handle.net/10356/157996 en B2053-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Wang, Yingfeng Hardware-friendly neural network design and optimization for low power IOT applications |
| title | Hardware-friendly neural network design and optimization for low power IOT applications |
| title_full | Hardware-friendly neural network design and optimization for low power IOT applications |
| title_fullStr | Hardware-friendly neural network design and optimization for low power IOT applications |
| title_full_unstemmed | Hardware-friendly neural network design and optimization for low power IOT applications |
| title_short | Hardware-friendly neural network design and optimization for low power IOT applications |
| title_sort | hardware friendly neural network design and optimization for low power iot applications |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/157996 |
| work_keys_str_mv | AT wangyingfeng hardwarefriendlyneuralnetworkdesignandoptimizationforlowpoweriotapplications |