Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes
Aiming at the existing handwritten digit recognition systems with low recognition accuracy, high system power consumption, and high hardware resource consumption, this paper proposes a low-power, high-precision, and lightweight handwritten digit recognition hardware acceleration scheme for multiscen...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Active and Passive Electronic Components |
| Online Access: | http://dx.doi.org/10.1155/apec/2070758 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849430616662081536 |
|---|---|
| author | Gangfeng Yang Liming Chen Jianhua Jiang Chengying Chen Xindong Huang |
| author_facet | Gangfeng Yang Liming Chen Jianhua Jiang Chengying Chen Xindong Huang |
| author_sort | Gangfeng Yang |
| collection | DOAJ |
| description | Aiming at the existing handwritten digit recognition systems with low recognition accuracy, high system power consumption, and high hardware resource consumption, this paper proposes a low-power, high-precision, and lightweight handwritten digit recognition hardware acceleration scheme for multiscenario based on FPGA. By optimizing the network structure of a convolutional neural network (CNN) and the number of parameters of the model, this scheme proposes a high-precision and lightweight network model, simplified CNN, and by optimizing the data access mode and memory usage, and by adopting the strategies of time-sharing and multiplexing, weights sharing, and parallel processing for the hardware acceleration of the algorithm, it effectively reduces the consumption of hardware resources and improves the performance of the system. The experimental results show that the recognition accuracy of the algorithm reaches 98.82%, the system response time is 0.481316 ms under the 33 + 200 MHz system clock, and the on-chip power consumption of the system is 0.843 W, and it can be used for real-time handwritten digit recognition in many occasions. |
| format | Article |
| id | doaj-art-646d94e32f0249c8b6f0d2e4e5dbc0f8 |
| institution | Kabale University |
| issn | 1563-5031 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Active and Passive Electronic Components |
| spelling | doaj-art-646d94e32f0249c8b6f0d2e4e5dbc0f82025-08-20T03:27:56ZengWileyActive and Passive Electronic Components1563-50312025-01-01202510.1155/apec/2070758Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple ScenesGangfeng Yang0Liming Chen1Jianhua Jiang2Chengying Chen3Xindong Huang4School of Opto-Electronic and Communication EngineeringSchool of Integrated CircuitsSchool of ElectronicsSchool of Opto-Electronic and Communication EngineeringSchool of Opto-Electronic and Communication EngineeringAiming at the existing handwritten digit recognition systems with low recognition accuracy, high system power consumption, and high hardware resource consumption, this paper proposes a low-power, high-precision, and lightweight handwritten digit recognition hardware acceleration scheme for multiscenario based on FPGA. By optimizing the network structure of a convolutional neural network (CNN) and the number of parameters of the model, this scheme proposes a high-precision and lightweight network model, simplified CNN, and by optimizing the data access mode and memory usage, and by adopting the strategies of time-sharing and multiplexing, weights sharing, and parallel processing for the hardware acceleration of the algorithm, it effectively reduces the consumption of hardware resources and improves the performance of the system. The experimental results show that the recognition accuracy of the algorithm reaches 98.82%, the system response time is 0.481316 ms under the 33 + 200 MHz system clock, and the on-chip power consumption of the system is 0.843 W, and it can be used for real-time handwritten digit recognition in many occasions.http://dx.doi.org/10.1155/apec/2070758 |
| spellingShingle | Gangfeng Yang Liming Chen Jianhua Jiang Chengying Chen Xindong Huang Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes Active and Passive Electronic Components |
| title | Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes |
| title_full | Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes |
| title_fullStr | Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes |
| title_full_unstemmed | Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes |
| title_short | Design of Low-Power, High-Precision, and Lightweight Image Recognition System for Multiple Scenes |
| title_sort | design of low power high precision and lightweight image recognition system for multiple scenes |
| url | http://dx.doi.org/10.1155/apec/2070758 |
| work_keys_str_mv | AT gangfengyang designoflowpowerhighprecisionandlightweightimagerecognitionsystemformultiplescenes AT limingchen designoflowpowerhighprecisionandlightweightimagerecognitionsystemformultiplescenes AT jianhuajiang designoflowpowerhighprecisionandlightweightimagerecognitionsystemformultiplescenes AT chengyingchen designoflowpowerhighprecisionandlightweightimagerecognitionsystemformultiplescenes AT xindonghuang designoflowpowerhighprecisionandlightweightimagerecognitionsystemformultiplescenes |