Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu


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 smartphones 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, 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 solution 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 solution 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.
OtsikkoINFOCOM 2020 - IEEE Conference on Computer Communications
TilaHyväksytty/In press - 15 heinäkuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Conference on Computer Communications - Online
Kesto: 6 heinäkuuta 20209 heinäkuuta 2020


ConferenceIEEE Conference on Computer Communications

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  • Siteeraa tätä

    Mohammed, T., Joe-Wong, C., Babbar, R., & Di Francesco, M. (Hyväksytty/painossa). Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. teoksessa INFOCOM 2020 - IEEE Conference on Computer Communications IEEE.