RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference-Reference-Cited by-同舟云学术

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference

Published:2024-05-13 Issue: Volume: Page:373-385
ISSN:
Container-title:2024 IEEE 30th Real-Time and Embedded Technology and Applications Symposium (RTAS)
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Author:

Kang Woosung¹,Lee Jinkyu²,Lee Youngmoon³,Oh Sangeun⁴,Lee Kilho⁵,Chwa Hoon Sung¹

Affiliation:

1. DGIST,Dept. of Electrical Engineering and Computer Science,Republic of Korea

2. Sungkyunkwan University (SKKU),Dept. of Computer Science and Engineering,Republic of Korea

3. Hanyang University,Dept. of Robotics,Republic of Korea

4. Ajou University,Department of Software & Computer Engineering,Republic of Korea

5. School of AI Convergence, Soongsil University,Republic of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10566868/10568032/10568074.pdf?arnumber=10568074

Reference35 articles.

1. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

2. M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network

3. UNet++: A Nested U-Net Architecture for Medical Image Segmentation

4. Superneurons

5. ZeRO: Memory optimizations Toward Training Trillion Parameter Models

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