An Energy-Efficient, Unified CNN Accelerator for Real-Time Multi-Object Semantic Segmentation for Autonomous Vehicle-Reference-Cited by-同舟云学术

An Energy-Efficient, Unified CNN Accelerator for Real-Time Multi-Object Semantic Segmentation for Autonomous Vehicle

Published:2024-05 Issue:5 Volume:71 Page:2093-2104
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Jung Jueun¹^ORCID,Kim Seungbin¹^ORCID,Jang Wuyoung²^ORCID,Seo Bokyoung²^ORCID,Lee Kyuho Jason³^ORCID

Affiliation:

1. Department of Electrical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea

2. Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea

3. Department of Electrical Engineering, Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, South Korea

Funder

National Research Foundation (NRF) of Korea

Korean Government [Ministry of Science and Information and Communications Technology (MSIT)]

Samsung Electronics

Institute of Information and Communications Technology Planning and Evaluation

Korean Government (MSIT)

IC Design Education Center (IDEC), South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8919/10510354/10391059.pdf?arnumber=10391059

Reference47 articles.

1. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2. Stereo R-CNN Based 3D Object Detection for Autonomous Driving

3. Fully-Convolutional Siamese Networks for Object Tracking

4. The Architectural Implications of Autonomous Driving

5. Building the Computing System for Autonomous Micromobility Vehicles: Design Constraints and Architectural Optimizations

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