Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
4-2019
Abstract
The paper describes a vision for dependable application of machine learning-based inferencing on resource-constrained edge devices. The high computational overhead of sophisticated deep learning learning techniques imposes a prohibitive overhead, both in terms of energy consumption and sustainable processing throughput, on such resource-constrained edge devices (e.g., audio or video sensors). To overcome these limitations, we propose a ``cognitive edge" paradigm, whereby (a) an edge device first autonomously uses statistical analysis to identify potential collaborative IoT nodes, and (b) the IoT nodes then perform real-time sharing of various intermediate state to improve their individual execution of machine intelligence tasks. We provide an example of such collaborative inferencing for an exemplar network of video sensors, showing how such collaboration can significantly improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. We also identify various challenges in realizing such a cognitive edge, including the need to ensure that the inferencing tasks do not suffer catastrophically in the presence of malfunctioning peer devices. We then introduce the soon-to-be deployed Cognitive IoT testbed at SMU, explaining the various features that enable empirical testing of various novel edge-based ML algorithms.
Keywords
Deep Learning, Edge Computing, Collaborative Sensing
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of SPIE: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, Maryland, US, 2019 April 15-17
Volume
1106
First Page
1
Last Page
14
Identifier
10.1117/12.2522656.short?SSO=1
Publisher
SPIE
City or Country
Baltimore, Maryland, US
Citation
MISRA, Archan; JAYARAJAH, Kasthuri; WEERAKOON MUDIYANSELAGE, Dulanga Kaveesha Weerakoon; DARATAN, Randy Tandriansyah; YAO, Shuochao; and ABDELZAHER, Tarek.
Dependable machine intelligence at the tactical edge. (2019). Proceedings of SPIE: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, Maryland, US, 2019 April 15-17. 1106, 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/4788
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1117/12.2522656.short?SSO=1