{"id":126774,"date":"2021-12-19T21:06:27","date_gmt":"2021-12-19T12:06:27","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-computer\/22853\/"},"modified":"2026-04-18T21:22:11","modified_gmt":"2026-04-18T12:22:11","slug":"22853","status":"publish","type":"ai-in-computer","link":"http:\/\/ee.presscat.kr\/en\/ai-in-computer\/22853\/","title":{"rendered":"Woo-Joong Kim and Chan-Hyun Youn  \u201cCooperative Scheduling Schemes for Explainable DNN Acceleration in Satellite Image Analysis and Retraining\u201d to appear in IEEE Transaction on Parallel and Distributed System (2021)"},"content":{"rendered":"<p style=\"text-align:justify\"><span style=\"font-size:10pt\"><span><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-US\">Abstract<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify\"><span style=\"font-size:10pt\"><span><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-US\">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 article, 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.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify\"><span style=\"font-size:10pt\"><span><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-US\"><\/p>\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uc724\ucc2c\ud6041.png\" alt=\"\" title=\"\"><\/div>\n<p><\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align:justify\"><span style=\"font-size:10pt\"><span><span><span style=\"font-family:Calibri,sans-serif\"><span lang=\"EN-US\"><\/p>\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uc724\ucc2c\ud6042.png\" alt=\"\" title=\"\"><\/div>\n<p><\/span><\/span><\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>926<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-126774","ai-in-computer","type-ai-in-computer","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-computer\/126774","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-computer"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-computer"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=126774"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}