MVSAS: Semantic-Aware Scheduling for Low Latency and High Precision in Wireless Multi-View Application

Multi-view models for various multi-view applications (e.g., pose recognition, facial recognition) achieve higher accuracy when more sensing data (views) from different sensors are flexibly collected via wireless networks and combined into inference input. However, when the view number scales up, the application suffers a long latency to collect all the latest views before inference (vanilla workflow). We observed that collecting all the latest views before inference is unnecessary, because different views are often not equally important and important views have major contribution to the output. In this paper, we present a Multi-View Semantic-Aware Scheduling (MVSAS) system that automatically prioritizes views according to their importance and schedules the early transmission of the important views. We tackled the challenge to infer view importance by analyzing the inference intermediates and extracting the semantics (e.g., number of persons) of each view. Once important views are collected, needless to wait for other less important views, the important views are combined with stale version of less important views as inference input, so as to retain high accuracy while reducing the latency to collect views. Evaluation shows that MVSAS achieved at most 36.9% latency reduction while retaining at most 98.7% accuracy compared to the vanilla workflow.