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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Bibliographic Details
Published in:2023 60th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 6
Main Authors: Odema, Mohanad, Ferlez, James, Shoukry, Yasser, Al Faruque, Mohammad Abdullah
Format: Conference Proceeding
Language:English
Published: IEEE 09.07.2023
Subjects:
Adaptation models
Autonomous Systems
Computational modeling
Edge Computing
Formal Methods
Multi-sensor Autonomous Driving Systems
Optimization methods
Pipelines
Process control
Runtime
Safe Control
Safety
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Summary: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.
DOI:10.1109/DAC56929.2023.10247751