RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded Devices
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 26 Nov 2024.
Builds on
10 of 29 references.
References · 10
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Built on by
Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Serverscited by 2
- 2025Leveraging Simultaneous Usage of Edge GPU Hardware Engines for Video Face Detection and Recognitioncited by 1
- 2025Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platformscited by 0
- 2026Multi-DNN Inference of Sparse Models on Edge SoCscited by 0
- 2026Multi-DNN Inference of Sparse Models on Edge SoCs10 Mar 2026
- 2025Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platforms1 Jul 2025
- 2025Leveraging Simultaneous Usage of Edge GPU Hardware Engines for Video Face Detection and Recognition7 May 2025
- 2025Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers6 Mar 2025