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

Shiwei Fang, Jin Huang, Colin Samplawski, Deepak Ganesan, Benjamin Marlin, Tarek Abdelzaher, Maggie B. Wigness

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMILCOM 2021 - 2021 IEEE Military Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665439565
StatePublished - 2021
Event2021 IEEE Military Communications Conference, MILCOM 2021 - San Diego, United States
Duration: Nov 29 2021Dec 2 2021

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM


Conference2021 IEEE Military Communications Conference, MILCOM 2021
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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