高性能双筒差距预测算法用于边缘计算
Yuxi Cheng1, Yang Song1, Yi Liu1
1Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究引入了边缘设备的新差异估计算法,显著提高了准确性并减少了计算负载. 这种新的方法提高了结构的适应性和实际应用的可行性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 端到端差异估计算法在边缘设备的结构适应性和准确性方面面临挑战.
- 现有的方法经常与计算复杂性和参数数量作斗争,以实现高效的部署.
研究的目的:
- 提出一种新的差异计算算法,以提高边缘神经网络加速器的准确性和效率.
- 解决结构性适应问题,减少差距估计中的计算复杂性.
主要方法:
- 使用低级近似来取代3D卷积和转换3D卷积.
- 集成的 WReLU 激活功能以减轻数据压缩.
- 采用单式成本量过和可靠性估计网络来规范成本量.
主要成果:
- 与典型网络相比,绝对误差减少了38.3%.
- 将三个像素的误差降低到1.41%.
- 将参数数量减少了67.3%,同时提高了准确性.
结论:
- 拟议的算法为差异估计提供了更高的准确性和更低的计算复杂性.
- 具有强大的结构适应性和实用性,使其更容易部署在边缘设备上.
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