Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2024

Abstract

Efficient and effective machine perception remains a formidable challenge in sustaining high fidelity and high throughput of perception tasks on affordable edge devices. This is especially due to the continuing increase in resolution of sensor streams (e.g., video input streams generated by 4K/8K cameras and neuromorphic event cameras that produce ≥ 10 MEvents/second) and computational complexity of Deep Neural Network (DNN) models, which overwhelms edge platforms, adversely impacting machine perception efficiency. Given the insufficiency of the available computation resources, a question then arises on whether selected regions/components of the perception task can be prioritized (and executed preferentially) to achieve highest task fidelity while adhering to the resource budget. This extended abstract explores the paradigm of Canvas-based Processing and criticality-awareness in the context of multi-sensor machine perception pipelines on resource-constrained platforms, in guiding perception pipelines and systems on “what" to pay attention to in the sensing field and “when", to maximize overall perception fidelity under computational constraints and moderate the processing throughput-vs-accuracy trade-off.

Keywords

Edge AI, Machine Perception, Canvas-based Processing

Discipline

Artificial Intelligence and Robotics | Software Engineering

Areas of Excellence

Digital transformation

Publication

MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, Minato-ku, Tokyo, Japan, June 3-7

First Page

751

Last Page

753

ISBN

9798400705816

Identifier

10.1145/3643832.3661386

Publisher

ACM

City or Country

New York

Embargo Period

8-26-2024

Copyright Owner and License

Author

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3643832.3661386

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