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Safety-Aware System Optimization for Autonomous Machines.
Safety-Aware System Optimization for Autonomous Machines.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017161302
- International Standard Book Number
- 9798382783307
- Dewey Decimal Classification Number
- 629.8
- Main Entry-Personal Name
- Hsiao, Yu-Shun.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Harvard University., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 131 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- General Note
- Advisor: Reddi, Vijay Janapa.
- Dissertation Note
- Thesis (Ph.D.)--Harvard University, 2024.
- Summary, Etc.
- 요약Autonomous machines such as vehicles, drones, and robotic manipulators promise to transform the world by unleashing humans from repetitive, dangerous, and labor-intensive tasks. However, their widespread deployment requires advances in safety, real-time performance, and resilience. This thesis tackles these key challenges through end-to-end system optimization that maintains safety while improving performance and fault tolerance. We optimize the entire Perception-Planning-Control (PPC) computing pipeline that takes in sensor readings and output control commands. First, this thesis develops a model to quantify the perception processing rate requirements for safe autonomous driving in complex scenarios, connecting the varying real-time latency requirements with the operating scenarios. Second, we accelerate the time-consuming 3D mapping in perception. A specialized accelerator is designed that achieves substantially higher throughput and energy efficiency over a CPU, enabling real-time perception for 3D mapping. Third, we accelerate the computationally expensive optimization-based motion planning algorithms with a variable precision search method that reduces memory bandwidth pressure without sacrificing positional and orientational precision. Lastly, we assess autonomous machines' fault tolerance characteristics against real-world noises and errors to generate reliable control commands. We propose a fault characterization framework that evaluates the impact of silent data corruptions (SDCs) on application-level metrics. To mitigate SDCs, we propose lightweight anomaly detection techniques to recover failures in the computing pipeline with insignificant overhead. This dissertation enables the development of safe, real-time, and resilient autonomous machines. The contributions chart a path toward robust deployment of autonomous machines that can transform society.
- Subject Added Entry-Topical Term
- Robotics.
- Subject Added Entry-Topical Term
- Computer science.
- Index Term-Uncontrolled
- Autonomous systems
- Index Term-Uncontrolled
- Computer architecture
- Index Term-Uncontrolled
- Silent data corruptions
- Index Term-Uncontrolled
- Real-time performance
- Added Entry-Corporate Name
- Harvard University Engineering and Applied Sciences - Computer Science
- Host Item Entry
- Dissertations Abstracts International. 85-12B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:658396
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a629.8
■1001 ▼aHsiao, Yu-Shun.▼0(orcid)0000-0002-2580-9872
■24510▼aSafety-Aware System Optimization for Autonomous Machines.
■260 ▼a[S.l.]▼bHarvard University. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a131 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Reddi, Vijay Janapa.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2024.
■520 ▼aAutonomous machines such as vehicles, drones, and robotic manipulators promise to transform the world by unleashing humans from repetitive, dangerous, and labor-intensive tasks. However, their widespread deployment requires advances in safety, real-time performance, and resilience. This thesis tackles these key challenges through end-to-end system optimization that maintains safety while improving performance and fault tolerance. We optimize the entire Perception-Planning-Control (PPC) computing pipeline that takes in sensor readings and output control commands. First, this thesis develops a model to quantify the perception processing rate requirements for safe autonomous driving in complex scenarios, connecting the varying real-time latency requirements with the operating scenarios. Second, we accelerate the time-consuming 3D mapping in perception. A specialized accelerator is designed that achieves substantially higher throughput and energy efficiency over a CPU, enabling real-time perception for 3D mapping. Third, we accelerate the computationally expensive optimization-based motion planning algorithms with a variable precision search method that reduces memory bandwidth pressure without sacrificing positional and orientational precision. Lastly, we assess autonomous machines' fault tolerance characteristics against real-world noises and errors to generate reliable control commands. We propose a fault characterization framework that evaluates the impact of silent data corruptions (SDCs) on application-level metrics. To mitigate SDCs, we propose lightweight anomaly detection techniques to recover failures in the computing pipeline with insignificant overhead. This dissertation enables the development of safe, real-time, and resilient autonomous machines. The contributions chart a path toward robust deployment of autonomous machines that can transform society.
■590 ▼aSchool code: 0084.
■650 4▼aRobotics.
■650 4▼aComputer science.
■653 ▼aAutonomous systems
■653 ▼aComputer architecture
■653 ▼aSilent data corruptions
■653 ▼aReal-time performance
■690 ▼a0771
■690 ▼a0800
■690 ▼a0984
■71020▼aHarvard University▼bEngineering and Applied Sciences - Computer Science.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161302▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
