Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
复制标题
实时网络物理系统中深度失分布探测器的设计方法
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
A. Easwaran
中科院分区：
文献类型：
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作者：
Michael Yuhas;Daniel Jun Xian Ng;A. Easwaran
当机器学习（ML）模型被提供了训练分布之外的数据时，它们更有可能做出不准确的预测;在网络物理系统（CPS）中，这可能导致灾难性的系统故障。为了减轻这种风险，一个分发外（OOD）检测器可以与ML模型并行运行，并标记可能导致不良结果的输入。虽然OOD检测器在准确性方面已经得到了很好的研究，但对资源受限的CPS的部署关注较少。在本研究中，提出了一种设计方法来调整深度OOD检测器，以满足嵌入式应用的准确性和响应时间要求。该方法使用遗传算法来优化检测器的预处理管道，并选择一种量化方法，平衡鲁棒性和响应时间。它还确定了几个候选任务图下的机器人操作系统（ROS）部署选定的设计。该方法被证明在两个可变的基于自编码器的OOD检测器从文献上的两个嵌入式平台。洞察到的权衡，在设计过程中发生的，它表明，这种设计方法可以导致响应时间大幅减少，相对于一个未优化的OOD检测器，同时保持相当的精度。
When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS), this could lead to catastrophic system failure. To mitigate this risk, an out-of-distribution (OOD) detector can run in parallel with an ML model and flag inputs that could lead to undesirable outcomes. Although OOD detectors have been well studied in terms of accuracy, there has been less focus on deployment to resource constrained CPSs. In this study, a design methodology is proposed to tune deep OOD detectors to meet the accuracy and response time requirements of embedded applications. The methodology uses genetic algorithms to optimize the detector’s preprocessing pipeline and selects a quantization method that balances robustness and response time. It also identifies several candidate task graphs under the Robot Operating System (ROS) for deployment of the selected design. The methodology is demonstrated on two variational autoencoder based OOD detectors from the literature on two embedded platforms. Insights into the trade-offs that occur during the design process are provided, and it is shown that this design methodology can lead to a drastic reduction in response time in relation to an unoptimized OOD detector while maintaining comparable accuracy.