Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2020-07
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
854-863
854-863
Series
INFOCOM 2020 - IEEE Conference on Computer Communications, Proceedings - IEEE INFOCOM, Volume 2020-July
Abstract
Deep neural networks (DNN) are the de-facto solution behind many intelligent applications of today, ranging from machine translation to autonomous driving. DNNs are accurate but resource-intensive, especially for embedded devices such as mobile phones and smart objects in the Internet of Things. To overcome the related resource constraints, DNN inference is generally offloaded to the edge or to the cloud. This is accomplished by partitioning the DNN and distributing computations at the two different ends. However, most of existing solutions simply split the DNN into two parts, one running locally or at the edge, and the other one in the cloud. In contrast, this article proposes a technique to divide a DNN in multiple partitions that can be processed locally by end devices or offloaded to one or multiple powerful nodes, such as in fog networks. The proposed scheme includes both an adaptive DNN partitioning scheme and a distributed algorithm to offload computations based on a matching game approach. Results obtained by using a self-driving car dataset and several DNN benchmarks show that the proposed solution significantly reduces the total latency for DNN inference compared to other distributed approaches and is 2.6 to 4.2 times faster than the state of the art.Description
Keywords
distributed algorithm, DNN inference, matching game, task offloading, task partitioning
Other note
Citation
Mohammed, T, Joe-Wong, C, Babbar, R & Francesco, M D 2020, Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading . in INFOCOM 2020 - IEEE Conference on Computer Communications ., 9155237, Proceedings - IEEE INFOCOM, vol. 2020-July, IEEE, pp. 854-863, IEEE Conference on Computer Communications, Toronto, Canada, 06/07/2020 . https://doi.org/10.1109/INFOCOM41043.2020.9155237
