| Title | Optimizing Intelligent Edge-clouds with Partitioning, Compression and Speculative Inference |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Fang, Shiwei, Huang, Jin, Samplawski, Colin, Ganesan, Deepak, Marlin, Benjamin, Abdelzaher, Tarek, Wigness, Maggie B. |
| Conference Name | MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) |
| Date Published | nov |
| Keywords | Computational modeling, Computer architecture, human factors, iobt, Market research, military communication, Neural networks, pubcrawl, resilience, Resiliency, Scalability, Sensors, Uncertainty |
| Abstract | 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. |
| DOI | 10.1109/MILCOM52596.2021.9653126 |
| Citation Key | fang_optimizing_2021 |
Optimizing Intelligent Edge-clouds with Partitioning, Compression and Speculative Inference