Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31–134 x smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward.
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MSNet: Structural Wired Neural Architecture Search for Internet of Things
The prosperity of Internet of Things (IoT) calls for
efficient ways of designing extremely compact yet accu-
rate DNN models. Both the cell-based neural architec-
ture search methods and the recently proposed graph based
methods fall short in finding high quality IoT models due
to the search flexibility, accuracy density, and node depen-
dency limitations. In this paper, we propose a new graph-
based neural architecture search methodology MSNAS for
crafting highly compact yet accurate models for IoT de-
vices. MSNAS supports flexible search space and can ac-
cumulate learned knowledge in a meta-graph to increase
accuracy density. By adopting structural wiring architec-
ture, MSNAS reduces the dependency between nodes, which
allows more compact models without sacrificing accuracy.
The preliminary experimental results on IoT applications
demonstrate that the MSNet crafted by MSNAS outperforms
MobileNetV2 and MnasNet by 3.0% in accuracy, with 20%
less peak memory consumption and similar Multi-Adds.
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- NSF-PAR ID:
- 10129647
- Date Published:
- Journal Name:
- Proceedings of ICCV 2019 Neural Architects Workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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