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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 605 - 609 (5page)
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초록· 키워드
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Due to the unprecedented computing requirement of autonomous driving applications, in-vehicle computing architecture is going under tremendous changes. For that, emerging computing units such as graphics processing units (GPUs) and neural processing units (NPUs) are employed, trying to satisfy such computing requirement. However, since the computing power of a single node is inherently limited, we cannot scale up beyond a single node’s computing capacity. To make a scalable computing architecture for computing-hungry applications, we propose to employ the cluster-based computing architecture that has been used in server-side applications for decades. As an initial effort, we develop a prototype object detection system by clustering three compute nodes equipped with an Nvidia SoC with an integrated GPU, connected through an ethernet bus and a CAN bus. A GigE Vision camera is connected to the ethernet bus and multicasts images to the three nodes. For each node to select its assigned images in a round-robin manner, a distributed consensus scheme is proposed. Our prototype implementation demonstrates a linear scalability of its frame rate up to three nodes with no additional delay overhead caused by the clustering architecture. By that, this study shows the potential of the cluster system for autonomous driving applications.
목차
Abstract
1. INTRODUCTION
2. BACKGROUND
3. SYSTEM DESIGN
4. EXPERIMENTS
5. RELATED WORK
6. CONCLUSION
REFERENCES
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