Deep Learning-based Real-time Segmentation for Edge Computing Devices

被引：1

作者：

Kwak, Jaeho ^{[1
]}

Yu, Hyunwoo ^{[1
]}

Cho, Yubin ^{[1
]}

Kang, Sukju ^{[1
]}

Cho, Jaechan ^{[2
]}

Park, Jun-Young ^{[2
]}

Lee, Ji-Won ^{[2
]}

机构：

[1] Sogang Univ, Dept Elect Engn, Seoul, South Korea

[2] LX Semicon, Seoul, South Korea

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA | 2022年

基金：

新加坡国家研究基金会;

关键词：

deep learning; semantic segmentation; real-time processing;

D O I：

10.1109/AICAS54282.2022.9869967

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, due to the rapid improvement of artificial intelligence technology, numerous studies are considered to solve various problems using deep learning. Typical deep neural networks for semantic segmentation require the high computation with a large capacity to extract abundant amounts of contextual information for accurate prediction. Our live demonstration will show real-time semantic segmentation operation on an NVIDIA Jetson-Xavier board with the BiSeNet-based method compressed using a novel knowledge distillation method.

引用

页数：1

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