一种用于远程传感图像的轻量级检测方法及其在边缘设备上的节能加速器
Ruiheng Yang1, Zhikun Chen1, Bin'an Wang1
1School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了用于远程传感边缘设备的轻量级卷积神经网络 (CNN),在缩小尺寸的情况下实现高精度. 一个节能的FPGA加速器提高了对功率敏感应用的性能.
科学领域:
- 计算机视觉 计算机视觉
- 遥感技术 遥感技术 遥感技术
- 嵌入式系统 嵌入式系统
背景情况:
- 卷积神经网络 (CNN) 对于遥感图像检测至关重要,但对于边缘设备来说往往太复杂了.
- 电能敏感和资源有限的遥感边缘设备需要高效的网络解决方案.
研究的目的:
- 为远程传感边缘设备提出一个轻量级的CNN.
- 开发一种使用现场可编程门阵列 (FPGA) 的节能CNN加速器.
主要方法:
- 应用了网络减重和优化技术,以最大限度地减少大小和硬件部署的复杂性.
- 为FPGA加速器开发了一个可重新配置和高效的卷积处理引擎.
- 针对拟议的轻量级网络结构,专门进行了硬件优化.
主要成果:
- 拟议的网络在更小的尺寸下实现了更高的准确性.
- 美国有线电视新闻网 (CNN) 加速器的吞吐量为29.53 GOPS,耗电量仅为2.98 W.
- 该解决方案使用了113个数字信号处理器 (DSP),并显示了1.1-2.5倍的DSP效率.
结论:
- 开发的轻量级CNN和FPGA加速器为远程传感边缘设备提供了卓越的解决方案.
- 拟议的系统非常适合在电力敏感和资源有限的远程传感边缘应用中部署.
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