New TAPAS strategy optimizes autonomous system perception for energy efficiency

Researchers have developed TAPAS, a novel throughput-adaptive perception strategy designed for autonomous systems operating on mobile and edge platforms. This system intelligently adjusts resource allocation in real-time based on varying scene complexity, aiming to optimize energy consumption while maintaining performance. TAPAS utilizes reinforcement learning with a gated recurrent unit agent to dynamically map perception tasks across heterogeneous hardware, demonstrating significant energy savings and robust throughput met rates on datasets like KITTI and nuScenes. AI

IMPACT Optimizes energy efficiency and performance for autonomous systems, potentially enabling longer operational times and wider deployment on edge devices.

RANK_REASON The cluster describes a new research paper detailing a novel method for autonomous systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• gated recurrent unit
• Kitti
• Nuscenes
• NVIDIA Jetson Orin NX 16GB
• reinforcement learning
• Sota
• Tapas

• paper
• infra

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