Design Methodology for Deep Out-of-Distribution Detectors in Real-Time
Cyber-Physical Systems
- URL: http://arxiv.org/abs/2207.14694v1
- Date: Fri, 29 Jul 2022 14:06:27 GMT
- Title: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time
Cyber-Physical Systems
- Authors: Michael Yuhas, Daniel Jun Xian Ng, Arvind Easwaran
- Abstract summary: An out-of-distribution (OOD) detector can run in parallel with an ML model and flag inputs that could lead to undesirable outcomes.
This study proposes a design methodology to tune deep OOD detectors to meet the accuracy and response time requirements of embedded applications.
- Score: 5.233831361879669
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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