嵌入式场可编程门阵列上的可配置多层感知子软传感器:针对流体流量估计中的多种部署目标
Tianheng Ling1, Chao Qian1, Theodor Mario Klann1
1Intelligent Embedded Systems of Computer Science, University of Duisburg-Essen, 47057 Duisburg, Germany.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了一种灵活的工作流程,用于在FPGA上创建可适应的多层感知器 (MLP) 软传感器. 该方法优化了各种嵌入式应用程序的精度,延迟和能源效率.
科学领域:
- 嵌入式系统工程 嵌入式系统工程
- 机器学习 硬件加速 机器学习 硬件加速
- 数字信号处理 数字信号处理
背景情况:
- 为FPGA开发高效的软传感器是具有挑战性的,因为不同的硬件限制.
- 现有的工作流程往往缺乏适应多样化的部署目标所需的适应性.
- 优化精度,延迟和功耗需要定制的硬件解决方案.
研究的目的:
- 为开发和部署基于嵌入式FPGA的可适应多层感知器 (MLP) 软传感器提供全面的工作流程.
- 引入一个新的,开源工具链,ElasticAI.Creator,以促进整个开发和部署过程.
- 为了证明工作流在实现精度,推断延迟和不同部署场景的能源效率之间取得平衡的有效性.
主要方法:
- 开发了一种灵活的工作流程,支持可配置的MLP架构 (层/神经元计数) 和量子化位宽.
- 利用开源的ElasticAI.Creator工具链进行量化意识的训练,仅整数推理和自动化的VHDL加速器生成.
- 使用两个不同的FPGA平台进行了流体流量估计的案例研究:AMD Spartan-7 XC7S15和Lattice iCE40UP5K.
主要成果:
- 在XC7S15.15上为精密聚焦的MLP实现了高精度 (MSE: 56.56,MAPE: 1.61%) 和低延迟 (23.87μs).
- 对于iCE40UP5K上的紧型MLP来说,已经证明了低功率 (2.06mW) 和能效 (0.172μJ/推理).
- 验证了工作流的能力,以根据特定的性能要求定制FPGA加速器.
结论:
- 提出的工作流允许开发针对FPGA的基于MLP的优化软传感器.
- ElasticAI.Creator 便于在各种硬件平台上进行适应性和高效的部署.
- 该研究成功地平衡了嵌入式ML应用程序的精度,延迟和能耗等关键性能指标.
关键词:
物联网的物联网,就是物联网.嵌入式基于FPGA的加速器嵌入式系统 嵌入式系统能源效率是指能效的能源效率.流体流量估计流体流量估计硬件 软件 共同设计量子化意识培训的培训量子化神经网络是一种量子化神经网络.软传感器 软传感器更多相关视频
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