EnergyShield：エネルギー効率のための証明可能な安全なニューラルネットワーク制御のオフロード
EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency
自動運転のNN制御をエッジにオフロードしつつ、シールドで安全性を保証し、車載評価より24〜54%の省エネを実現するフレームワークを提案・Carlaで検証した。
著者: Mohanad Odema, James Ferlez, Goli Vaisi, Yasser Shoukry, Mohammad Abdullah Al Faruque
分類: eess.SY, cs.DC, cs.LG, cs.RO, cs.SY
原文アブストラクト
To mitigate the high energy demand of Neural Network (NN) based Autonomous Driving Systems (ADSs), we consider the problem of offloading NN controllers from the ADS to nearby edge-computing infrastructure, but in such a way that formal vehicle safety properties are guaranteed. In particular, we propose the EnergyShield framework, which repurposes a controller ''shield'' as a low-power runtime safety monitor for the ADS vehicle. Specifically, the shield in EnergyShield provides not only safety interventions but also a formal, state-based quantification of the tolerable edge response time before vehicle safety is compromised. Using EnergyShield, an ADS can then save energy by wirelessly offloading NN computations to edge computers, while still maintaining a formal guarantee of safety until it receives a response (on-vehicle hardware provides a just-in-time fail safe). To validate the benefits of EnergyShield, we implemented and tested it in the Carla simulation environment. Our results show that EnergyShield maintains safe vehicle operation while providing significant energy savings compared to on-vehicle NN evaluation: from 24% to 54% less energy across a range of wireless conditions and edge delays.
