# SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge

## TL;DR

This paper introduces a safety-aware energy optimization framework for multi-sensor neural controllers at the edge, balancing energy efficiency with formal safety guarantees in autonomous systems.

## Contribution

It presents a novel framework that dynamically adjusts energy optimization based on the system's safety state, ensuring safety properties are preserved.

## Key findings

- Achieves up to 89.9% energy savings while maintaining safety.
- Demonstrates effectiveness in autonomous driving simulation.
- Adapts workloads based on safety state characterization.

## 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.