SF-MMCN: Low-Power Sever Flow Multi-Mode Diffusion Model Accelerator
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909326779613184 |
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| author | Hsu, Huan-Ke Wey, I-Chyn Teo, T. Hui |
| author_facet | Hsu, Huan-Ke Wey, I-Chyn Teo, T. Hui |
| contents | Generative Artificial Intelligence (AI) has become incredibly popular in recent years, and the significance of traditional accelerators in dealing with large-scale parameters is urgent. With the diffusion model's parallel structure, the hardware design challenge has skyrocketed because of the multiple layers operating simultaneously. Convolution Neural Network (CNN) accelerators have been designed and developed rapidly, especially for high-speed inference. Often, CNN models with parallel structures are deployed. In these CNN accelerators, many Processing Elements (PE) are required to perform parallel computations, mainly the multiply and accumulation (MAC) operation, resulting in high power consumption and a large silicon area. In this work, a Server Flow Multi-Mode CNN Unit (SF-MMCN) is proposed to reduce the number of PE while improving the operation efficiency of the CNN accelerator. The pipelining technique is introduced into Server Flow to process parallel computations. The proposed SF-MMCN is implemented with TSMC 90-nm CMOS technology. It is evaluated with VGG-16, ResNet-18, and U-net. The evaluation results show that the proposed SF-MMCN can reduce the power consumption by 92%, and the silicon area by 70%, while improving the efficiency of operation by nearly 81 times. A new FoM, area efficiency (GOPs/mm^2) is also introduced to evaluate the performance of the accelerator in terms of the ratio throughput (GOPs) and silicon area (mm^2). In this FoM, SF-MMCN improves area efficiency by 18 times (18.42). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SF-MMCN: Low-Power Sever Flow Multi-Mode Diffusion Model Accelerator Hsu, Huan-Ke Wey, I-Chyn Teo, T. Hui Hardware Architecture Computer Vision and Pattern Recognition Generative Artificial Intelligence (AI) has become incredibly popular in recent years, and the significance of traditional accelerators in dealing with large-scale parameters is urgent. With the diffusion model's parallel structure, the hardware design challenge has skyrocketed because of the multiple layers operating simultaneously. Convolution Neural Network (CNN) accelerators have been designed and developed rapidly, especially for high-speed inference. Often, CNN models with parallel structures are deployed. In these CNN accelerators, many Processing Elements (PE) are required to perform parallel computations, mainly the multiply and accumulation (MAC) operation, resulting in high power consumption and a large silicon area. In this work, a Server Flow Multi-Mode CNN Unit (SF-MMCN) is proposed to reduce the number of PE while improving the operation efficiency of the CNN accelerator. The pipelining technique is introduced into Server Flow to process parallel computations. The proposed SF-MMCN is implemented with TSMC 90-nm CMOS technology. It is evaluated with VGG-16, ResNet-18, and U-net. The evaluation results show that the proposed SF-MMCN can reduce the power consumption by 92%, and the silicon area by 70%, while improving the efficiency of operation by nearly 81 times. A new FoM, area efficiency (GOPs/mm^2) is also introduced to evaluate the performance of the accelerator in terms of the ratio throughput (GOPs) and silicon area (mm^2). In this FoM, SF-MMCN improves area efficiency by 18 times (18.42). |
| title | SF-MMCN: Low-Power Sever Flow Multi-Mode Diffusion Model Accelerator |
| topic | Hardware Architecture Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10542 |