运行时强大的边缘推理系统,在动态重配置FPGA上进行基于掩盖的部分更新
1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了一个新的边缘推理框架,用于在动态环境中实时适应. 它使用服务器端的FPGA进行掩盖更新,降低了强大的边缘AI的延迟和通信成本.
科学领域:
- 边缘计算 边缘计算
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 边缘推理系统需要在环境变化 (如传感器噪声和新对象类) 中实时性能.
- 边缘设备上的静态,离线训练模型由于输入分布漂移而降低准确性.
研究的目的:
- 提出一个运行时强大的边缘推理框架,允许在没有执行中断的情况下进行持续的调整.
- 为了利用服务器端的FPGA加速,在边缘设备上进行高效,动态的模型更新.
主要方法:
- 在边缘设备上将内存分成主动和自适应区域.
- 服务器端的FPGA执行层级重要性分析和部分再培训.
- 动态部分重新配置 (DPR) 用于自适应的面具生成和最小化重新配置延迟.
主要成果:
- 与GPU全面重新训练相比,适应延迟降低了1.3倍.
- 通信成本降至28%的完整模型传输.
- 证明了实时适应性,低延迟和通信效率.
结论:
- 拟议的框架有效地将基于掩盖的选择性更新与FPGA DPR加速相结合.
- 在云端协作环境中实现强大的实时学习和适应.
- 为动态边缘推理挑战提供了可行的解决方案.
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