Optimizing Intelligent Edge-clouds with Partitioning, Compression and Speculative Inference

Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements,...

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Bibliographic Details
Published inMILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) pp. 892 - 896
Main Authors Fang, Shiwei, Huang, Jin, Samplawski, Colin, Ganesan, Deepak, Marlin, Benjamin, Abdelzaher, Tarek, Wigness, Maggie B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.11.2021
Subjects
Computational modeling
Computer architecture
Military communication
Neural networks
Sensors
Uncertainty
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Summary:Internet of Battlefield Things (IoBTs) are well positioned to take advantage of recent technology trends that have led to the development of low-power neural accelerators and low-cost high-performance sensors. However, a key challenge that needs to be dealt with is that despite all the advancements, edge devices remain resource-constrained, thus prohibiting complex deep neural networks from deploying and deriving actionable insights from various sensors. Furthermore, deploying sophisticated sensors in a distributed manner to improve decision-making also poses an extra challenge of coordinating and exchanging data between the nodes and server. We propose an architecture that abstracts away these thorny deployment considerations from an end-user (such as a commander or warfighter). Our architecture can automatically compile and deploy the inference model into a set of distributed nodes and server while taking into consideration of the resource availability, variation, and uncertainties.
ISSN:2155-7586
DOI:10.1109/MILCOM52596.2021.9653126