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Updated: Sep 11, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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对嵌入式系统的神经网络推理的全面分析:响应时间,校准和模型优化

Patrick Huber1,2, Ulrich Göhner3, Mario Trapp2,4

  • 1Institute for Driver Assistance and Connected Mobility (IFM), Kempten University of Applied Sciences, Junkerstraße 1A, 87734 Benningen, Germany.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括

在边缘设备上优化人工神经网络 (ANN) 推断速度对于预测性维护等实时应用至关重要. 这项研究对ANN的性能进行了基准评估,揭示了谨慎的参数调整对于嵌入式系统的高效部署至关重要.

关键词:
一个ANN的推理推理.在 Tensorflow Lite 中使用.基准测试 (benchmarking) 是一种比较的方法.嵌入式系统 嵌入式系统模型校准模型的校准.响应时间 响应时间

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