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相关概念视频

Load-frequency control01:28

Load-frequency control

126
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
126

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相关实验视频

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Deep Neural Networks for Image-Based Dietary Assessment
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LDF-BNN:基于改进的BNext的实时和高精度二元神经网络加速器.

Rui Wan1,2, Rui Cen1,2, Dezheng Zhang1,2

  • 1Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.

Micromachines
|October 26, 2024
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概括

本研究引入了二进制神经网络 (BNN) 与分层数据融合 (LDF-BNN),以提高工业缺陷检测的准确性,同时降低计算成本. 在ImageNet和缺陷检测任务上,LDF-BNN实现了高性能,使其适用于边缘设备.

关键词:
在FPGA中,FPGA是指FPGA.二元神经网络是二元神经网络.硬件加速器是一个硬件加速器.具有高精度的高精度.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 硬件加速器 硬件加速器

背景情况:

  • 深度神经网络 (DNN) 擅长用于工业缺陷检测的特征提取,但对于边缘设备来说计算密集.
  • 传统的二进制神经网络 (BNNs) 提供了效率,但受到精度下降的影响.

研究的目的:

  • 开发一个高效的二进制神经网络 (BNN) 具有层级数据融合 (LDF) 机制,以解决工业缺陷检测中的准确性和计算挑战.
  • 为拟议的LDF-BNN.设计一个优化的硬件加速器架构.

主要方法:

  • 基于BNext构建了一个分层数据融合BNN (LDF-BNN),结合了分层数据融合机制,以最大限度地减少带宽压力和准确性损失.
  • 设计了一种高效的硬件加速器架构,具有多存储并行性,以提高复杂的BNN模型的计算效率和性能.

主要成果:

  • 在ImageNet数据集上,LDF-BNN实现了72.23%的准确性,72.6 FPS和1826个GOP.
  • 在工业缺陷检测方面具有很高的适用性,在混合WM-38数据集上达到98.70%的准确性.
  • 在综合性比较,平衡精度和计算效率方面超越现有方法.

结论:

  • 拟议的LDF-BNN有效地减轻了BNN中的精度降解问题,同时保持了较低的计算和内存需求.
  • 开发的硬件加速器架构提高了LDF-BNN模型的性能,使它们适合在边缘设备上实时进行工业缺陷检测.