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전자정보연구정보센터 ICT 융합 전문연구정보의 집대성

EIRIC 세미나

국내외의 전자정보 및 ICT 분야 연구자들이 학술정보 또는 연구와 관련된 교육 콘텐츠를 무료로 접할 수 있는 온라인 세미나입니다.

화상회의시스템을 활용하여 소규모 세미나, 워크숍 등 목적에 따라 다양하게 활용할 수 있습니다.

Webinar

홈 홈 > EIRIC 광장 > EIRIC 세미나
컴퓨터 전자/전기 통신 AI 융합
Real-Time Object Detection System with Multi-Path Neural Networks
  • 일시2021년 6월 28일 (월) 오후 4시
  • 연사김한준 교수연세대학교 전기전자공학부
  • 약력PDF
개최완료
세미나 개요

Thanks to the recent advances in Deep Neural Networks (DNNs), DNN-based object detection systems becomes highly accurate and widely used in real-time environments such as autonomous vehicles, drones and security robots. Although the systems should detect objects within a certain time limit that can vary depending on their execution environments such as vehicle speeds, existing systems blindly execute the entire long- latency DNNs without reflecting the time-varying time limits, and thus they cannot guarantee real-time constraints. This work proposes a novel real-time object detection system that employs multi-path neural networks based on a new worst-case execution time (WCET) model for DNNs on a GPU. This work designs the WCET model for a single-layer of DNNs analyzing processor and memory contention on GPUs, and extends the WCET model to the end-to-end networks. This work also designs the multi- path networks with three new operators such as skip, switch, and dynamic generate proposals that dynamically change their execution paths and the number of target objects. Finally, this work proposes a path decision model that chooses the optimal execution path at run-time reflecting dynamically changing en- vironments and time constraints. Our detailed evaluation using widely-used driving datasets shows that the proposed real-time object detection system performs as good as a baseline object detection system without violating the time-varying time limits. Moreover, the WCET model predicts the worst-case execution latency of convolutional and group normalization layers with only 28% and 64% errors on average, respectively.

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