Design of a DBN Hardware Accelerator for Handwritten Digit Recognition
2018
Hochschulschrift
Zugriff:
107
The deep belief network (DBN) is implemented in this thesis. First, the MNIST database is used as a functional verification of the network architecture. Later, the relevant database applications for voice identification will be applied. In the training model extraction and hardware verification, the Matlab simulation is performed to determine the appropriate network size and layers which can achieve satisfactory identification results. Subsequently, the trained model is stored in ROMs and integrated into the proposed hardware architecture. Then the test data of the MNIST database are used to verify the accuracy of the DBN hardware circuit. With the development of artificial intelligence, researches on speech recognition and deep learning become increasingly popular. With the aging society, the hearing aids also attracting attention. Traditional hearing aids are susceptible to environmental sounds. In addition, the hearing aids need to be wore for a long time, and the design of the assistive devices needs to be light and low-power consumption. Therefore, deep learning is used for the environmental sound field in the hearing aids to improve the resistance to the environmental sound field, and suitable hearing compensation can be applied. Using application specific IC (ASIC) to implement audio equipment for hearing aids can achieve lightweight and low-power consumption, and is a trend in the design of hearing aids. To future, the relevant database applications for voice identification will be applied. Furthermore, reducing the access times of the external memory, dynamically adjust the accuracy of calculations and reducing the number of MACs to reduce the power consumption of the overall operation will be gradually studied in the future works.
Titel: |
Design of a DBN Hardware Accelerator for Handwritten Digit Recognition
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Autor/in / Beteiligte Person: | LI,YI-ZENG ; 李奕增 |
Link: | |
Veröffentlichung: | 2018 |
Medientyp: | Hochschulschrift |
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