SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge
Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such o...
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| Vydáno v: | 2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6 |
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| Jazyk: | angličtina |
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IEEE
09.07.2023
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| Abstract | Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such optimizations, which in turn limits their application in real settings. In this paper, we propose a novel energy optimization framework that is aware of the autonomous system's safety state, and leverages it to regulate the application of energy optimization methods so that the system's formal safety properties are preserved. In particular, through the formal characterization of a system's safety state as a dynamic processing deadline, the computing workloads of the underlying models can be adapted accordingly. For our experiments, we model two popular runtime energy optimization methods, offloading and gating, and simulate an autonomous driving system (ADS) use-case in the CARLA simulation environment with performance characterizations obtained from the standard Nvidia Drive PX2 ADS platform. Our results demonstrate that through a formal awareness of the perceived risks in the test case scenario, energy efficiency gains are still achieved (reaching 89.9%) while maintaining the desired safety properties. |
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| AbstractList | Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such optimizations, which in turn limits their application in real settings. In this paper, we propose a novel energy optimization framework that is aware of the autonomous system's safety state, and leverages it to regulate the application of energy optimization methods so that the system's formal safety properties are preserved. In particular, through the formal characterization of a system's safety state as a dynamic processing deadline, the computing workloads of the underlying models can be adapted accordingly. For our experiments, we model two popular runtime energy optimization methods, offloading and gating, and simulate an autonomous driving system (ADS) use-case in the CARLA simulation environment with performance characterizations obtained from the standard Nvidia Drive PX2 ADS platform. Our results demonstrate that through a formal awareness of the perceived risks in the test case scenario, energy efficiency gains are still achieved (reaching 89.9%) while maintaining the desired safety properties. |
| Author | Ferlez, James Shoukry, Yasser Al Faruque, Mohammad Abdullah Odema, Mohanad |
| Author_xml | – sequence: 1 givenname: Mohanad surname: Odema fullname: Odema, Mohanad organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA – sequence: 2 givenname: James surname: Ferlez fullname: Ferlez, James organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA – sequence: 3 givenname: Yasser surname: Shoukry fullname: Shoukry, Yasser organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA – sequence: 4 givenname: Mohammad Abdullah surname: Al Faruque fullname: Al Faruque, Mohammad Abdullah organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,CA,USA |
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| Snippet | Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform... |
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| SubjectTerms | Adaptation models Autonomous Systems Computational modeling Edge Computing Formal Methods Multi-sensor Autonomous Driving Systems Optimization methods Pipelines Process control Runtime Safe Control Safety |
| Title | SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge |
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