On the Task Mapping and Scheduling for DAG-based Embedded Vision Applications on Heterogeneous Multi/Many-core Architectures

Abstract

In this work, we show that applying the heterogeneous earliest finish time (HEFT) heuristic for the task scheduling of embedded vision applications can improve the system performance up to 70% w.r.t. the scheduling solutions at the state of the art. We propose an algorithm called exclusive earliest finish time (XEFT) that introduces the notion of exclusive overlap between application primitives to improve the load balancing. We show that XEFT can improve the system performance up to 33% over HEFT, and 82% over the state of the art approaches. We present the results on different benchmarks, including a real-world localization and mapping application (ORB-SLAM) combined with the NVIDIA object detection application based on deep-learning.