English

RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded Devices

Machine Learning 2024-11-28 v1 Distributed, Parallel, and Cluster Computing Emerging Technologies

Abstract

Modern edge data centers simultaneously handle multiple Deep Neural Networks (DNNs), leading to significant challenges in workload management. Thus, current management systems must leverage the architectural heterogeneity of new embedded systems to efficiently handle multi-DNN workloads. This paper introduces RankMap, a priority-aware manager specifically designed for multi-DNN tasks on heterogeneous embedded devices. RankMap addresses the extensive solution space of multi-DNN mapping through stochastic space exploration combined with a performance estimator. Experimental results show that RankMap achieves x3.6 higher average throughput compared to existing methods, while preventing DNN starvation under heavy workloads and improving the prioritization of specified DNNs by x57.5.

Keywords

Cite

@article{arxiv.2411.17867,
  title  = {RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded Devices},
  author = {Andreas Karatzas and Dimitrios Stamoulis and Iraklis Anagnostopoulos},
  journal= {arXiv preprint arXiv:2411.17867},
  year   = {2024}
}

Comments

8 pages, 10 figures, 1 table, Accepted for publication at the 28th Design Automation and Test in Europe Conference (DATE 2025), Best Paper Award Candidate

R2 v1 2026-06-28T20:13:48.139Z