TY - GEN
T1 - Impact of software approximations on the resiliency of a video summarization system
AU - Venkatagiri, Radha
AU - Swaminathan, Karthik
AU - Lin, Chung Ching
AU - Wang, Liang
AU - Buyuktosunoglu, Alper
AU - Bose, Pradip
AU - Adve, Sarita
N1 - Funding Information:
This work was supported in part by the Defense Advanced Research Projects Agency (DARPA), by the National Science Foundation under Grant CCF-1320941, by the Center for Future Architectures Research (C-FAR) and the Applications Driving Architectures (ADA) center, one of six centers of JUMP, a Semiconductor Research Corporation program co-sponsored by DARPA. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. This paper is: Approved for Public Release, Distribution Unlimited.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - In this work, we examine the resiliency of a state-of-the-art end-to-end video summarization (VS) application that serves as a representative emerging workload in the domain of real time edge computing. The VS application constitutes key video and image analytic elements that are processed by embedded systems aboard unmanned aerial vehicles (UAVs). Real-time performance and energy constraints motivate the consideration of approximations to the VS algorithm. However, mission-critical UAV applications also demand stringent levels of resilience to soft errors that are exacerbated with higher altitude. In this work, we study the effects of three different types of software approximations on the application level resiliency (to soft errors) of the VS algorithm. We show that our approximations yield significant energy savings (up to 68%), with commensurate improvement in performance, without a degradation in the application resilience. Further, by proposing a novel quality metric (appropriate for the UAV vision analytics domain) for the summarized video output, we show that even though the rate of Silent Data Corruptions (SDCs) increases slightly (<2%), the impact of these SDCs on output quality is limited. Thus, we conclude that software approximation can be utilized to achieve significant gains in performance and energy without affecting application resiliency.
AB - In this work, we examine the resiliency of a state-of-the-art end-to-end video summarization (VS) application that serves as a representative emerging workload in the domain of real time edge computing. The VS application constitutes key video and image analytic elements that are processed by embedded systems aboard unmanned aerial vehicles (UAVs). Real-time performance and energy constraints motivate the consideration of approximations to the VS algorithm. However, mission-critical UAV applications also demand stringent levels of resilience to soft errors that are exacerbated with higher altitude. In this work, we study the effects of three different types of software approximations on the application level resiliency (to soft errors) of the VS algorithm. We show that our approximations yield significant energy savings (up to 68%), with commensurate improvement in performance, without a degradation in the application resilience. Further, by proposing a novel quality metric (appropriate for the UAV vision analytics domain) for the summarized video output, we show that even though the rate of Silent Data Corruptions (SDCs) increases slightly (<2%), the impact of these SDCs on output quality is limited. Thus, we conclude that software approximation can be utilized to achieve significant gains in performance and energy without affecting application resiliency.
KW - Approximate Computing
KW - Hardware Resiliency
KW - Real time vision analytics
KW - Soft errors
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U2 - 10.1109/DSN.2018.00067
DO - 10.1109/DSN.2018.00067
M3 - Conference contribution
AN - SCOPUS:85051076775
T3 - Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2018
SP - 598
EP - 609
BT - Proceedings - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2018
Y2 - 25 June 2018 through 28 June 2018
ER -