TY - GEN
T1 - FastVA
T2 - 38th IEEE Conference on Computer Communications, INFOCOM 2020
AU - Tan, Tianxiang
AU - Cao, Guohong
N1 - Funding Information:
VIII. ACKNOWLEDGEMENTS This work was supported in part by the National Science Foundation under grants CNS-1526425 and CNS-1815465.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
AB - Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
UR - http://www.scopus.com/inward/record.url?scp=85090293071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090293071&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM41043.2020.9155476
DO - 10.1109/INFOCOM41043.2020.9155476
M3 - Conference contribution
AN - SCOPUS:85090293071
T3 - Proceedings - IEEE INFOCOM
SP - 1947
EP - 1956
BT - INFOCOM 2020 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 July 2020 through 9 July 2020
ER -
