Cooperative Scheduling Schemes for Explainable DNN Acceleration in Satellite Image Analysis and Retraining

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The deep learning-based satellite image analysis and retraining systems are getting emerging technologies to enhance the capability of the sophisticated analysis of terrestrial objects. In principle, to apply the explainable DNN model for the process of satellite image analysis and retraining, we consider a new acceleration scheduling mechanism. Especially, the conventional DNN acceleration schemes cause serious performance degradation due to computational complexity and costs in satellite image analysis and retraining. In this paper, to overcome the performance degradation, we propose cooperative scheduling schemes for explainable DNN acceleration in analysis and retraining process. For the purpose of it, we define the latency and energy cost modeling to derive the optimized processing time and cost required for explainable DNN acceleration. Especially, we show a minimum processing cost considered in the proposed scheduling via layer-level management of the explainable DNN on FPGA-GPU acceleration system. In addition, we evaluate the performance using an adaptive unlabeled data selection scheme with confidence threshold and a semi-supervised learning driven data parallelism scheme in accelerating retraining process. The experimental results demonstrate that the proposed schemes reduce the energy cost of the conventional DNN acceleration systems by up to about 40% while guaranteeing the latency constraints.
Publisher
IEEE COMPUTER SOC
Issue Date
2022-07
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, v.33, no.7, pp.1605 - 1618

ISSN
1045-9219
DOI
10.1109/tpds.2021.3122454
URI
http://hdl.handle.net/10203/288740
Appears in Collection
EE-Journal Papers(저널논문)
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