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- W3137245035 abstract "In recent years, advances in deep learning (DL) technology have greatly improved research and services related to artificial intelligence (AI). In particular, real-time object recognition has become an important technology in smart cities. To achieve this, low-cost network deployment and low-latency data transfer are the key technologies. In this paper, we focus on Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) based inference systems to deploy cost-efficient networks that accommodate many network cameras. A significant issue for a GPU-based inference system via TWDM-PON is optimally allocating upstream wavelength and bandwidth to enable real-time inference. However, it has not been considered to increase the batch size of arrival data at edge servers ensuring low-latency transmission. Therefore, this paper proposes a concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via TWDM-PONe We also propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrival at the edge. The performance of the proposed scheme is verified with computer simulation." @default.
- W3137245035 created "2021-03-29" @default.
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- W3137245035 date "2021-01-09" @default.
- W3137245035 modified "2023-09-23" @default.
- W3137245035 title "Real-Time and Energy-Efficient Inference at GPU-Based Network Edge using PON" @default.
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- W3137245035 doi "https://doi.org/10.1109/ccnc49032.2021.9369452" @default.
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