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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2403.06352 |
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| _version_ | 1866916155725185024 |
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| author | Lei, Xing Liu, Longjun Zhou, Zhiheng Sun, Hongbin Zheng, Nanning |
| author_facet | Lei, Xing Liu, Longjun Zhou, Zhiheng Sun, Hongbin Zheng, Nanning |
| contents | In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to the hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-ResnetV1 and MobilenetV2, which can effectively reduce parameters and delay while maintaining the accuracy of inference. We deploy our L-Mobilenet model to ZYNQ embedded platform for fully evaluating the performance of our design. By measuring in cifar10 and cifar100 datasets, L-Mobilenet model is able to gain 3x speed up and 3.7x fewer parameters than MobileNetV2 while maintaining a similar accuracy. It also can obtain 2x speed up and 1.5x fewer parameters than ShufflenetV2 while maintaining the same accuracy. Experiments show that our network model can obtain better performance because of the special considerations for hardware accelerating and software-hardware co-design strategies in our L-Mobilenet bottleneck architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06352 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems Lei, Xing Liu, Longjun Zhou, Zhiheng Sun, Hongbin Zheng, Nanning Computer Vision and Pattern Recognition In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to the hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-ResnetV1 and MobilenetV2, which can effectively reduce parameters and delay while maintaining the accuracy of inference. We deploy our L-Mobilenet model to ZYNQ embedded platform for fully evaluating the performance of our design. By measuring in cifar10 and cifar100 datasets, L-Mobilenet model is able to gain 3x speed up and 3.7x fewer parameters than MobileNetV2 while maintaining a similar accuracy. It also can obtain 2x speed up and 1.5x fewer parameters than ShufflenetV2 while maintaining the same accuracy. Experiments show that our network model can obtain better performance because of the special considerations for hardware accelerating and software-hardware co-design strategies in our L-Mobilenet bottleneck architecture. |
| title | Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.06352 |