등록
엣지 컴퓨팅 환경에서 최적의 분산 컴퓨팅을 위한 강화학습 기반의 심층신경망 분할 방법
- 발명자
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이창식, 홍성백, 홍승우, 류호용
- 출원번호
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16830253 (2020.03.25)
- 공개번호
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20200311546 (2020.10.01)
- 등록번호
- 11521066 (2022.12.06)
- 출원국
- 미국
- 협약과제
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19HH2100, 인공지능 기반 지능형 에지 네트워킹 기술개발,
김태연
- 초록
- A processor partitions a deep neural network having a plurality of exit points and at least one partition point in a branch corresponding to each of the exit points, for distributed processing in an edge device and a cloud. The processor sets environmental variables and training variables for training, selects an action to move at least one of an exit point and a partition point from a combination of the exit point and the partition point corresponding to a current state, performs the training by accumulating experience data using a reward according to the selected action and then moves to a next state, and outputs a combination of an optimal exit point and a partition point as a result of the training.
- KSP 제안 키워드
- Current state, Deep neural network(DNN), Edge devices, Partition point, distributed processing, environmental variables, neural network