低功率分支CNN硬件加速器与UAV灾难检测的早期退出使用16纳米CMOS技术
Yu-Pei Liang1, Wen-Chin Chao1, Ching-Che Chung1
1Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chia-Yi 621301, Taiwan.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种高效的空中灾难检测系统,使用分支卷积神经网络 (B-CNN) 和硬件加速. 该框架实现了高精度与低功耗的实时监控.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 灾害监测需要高效准确的航空图像分析.
- 现有的方法经常面临计算资源和延迟方面的挑战.
- 需要低功耗,实时灾难预测系统.
研究的目的:
- 通过使用空中图像开发一个高效的灾难检测框架.
- 通过分支卷积神经网络 (B-CNN) 增强特征学习并减少模型参数.
- 通过重量量化和早期退出机制优化资源使用.
主要方法:
- 利用分支卷积神经网络 (B-CNN) 进行增强的特征学习.
- 集成的DoReFa-Net用于重量定量和固定点表示.
- 实施了早期退出机制,以实现低延迟,高能效的预测.
- 开发了一个B-CNN硬件加速器,使用TSMC 16nm CMOS技术与电源门.
主要成果:
- B-CNN 硬件加速器以 500 MHz 的频率运行,功耗为 37.56 mW.
- 实现了88.18%的灾难预测准确度.
- 证明了有效的训练和推断,减少了模型参数.
结论:
- 拟议的框架对于低功耗,实时空中灾难监测是有效的.
- B-CNN硬件加速器为节能灾害预测提供了一个合适的解决方案.
- 该系统的性能突显了其在关键灾害响应场景中的潜力.
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