| Autor: |
Cheng Jiafeng, Wang Hongliang |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Dianzi Jishu Yingyong, Vol 47, Iss 3, Pp 18-21 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
0258-7998 |
| DOI: |
10.16157/j.issn.0258-7998.200841 |
| Popis: |
Based on the idea of software and hardware co-design, this article uses HLS tools to design and implement a convolutional neural network accelerator on the PYNQ-Z2 platform, and uses the matrix cutting optimization method for convolution operations to balance resource consumption and computing resources , so that the performance of the accelerator is optimized. This article uses the MNIST data set to test the performance of the accelerator IP core. The experimental results show that: for a single image test, the accelerator achieves an acceleration effect of 5.785 compared with the ARM platform, and an acceleration of 9.72 for a 1000 image test. As a result, as the number of test images continues to increase, the performance of the accelerator will become better and better. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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