mlsanityData to 14 Oct 2026

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

arXiv 2411.17867 ↗First posted 26 Nov 2024cs.LG cs.DC cs.ETcited by 4

Not on the map. Why?

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

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Neural Discrete Representation Learningcited by 4,712
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  4. 2023VILA: On Pre-training for Visual Language Modelscited by 692
  5. 2018Efficient Attention: Attention with Linear Complexitiescited by 368
  6. 2021Monte Carlo Tree Search: A Review of Recent Modifications and Applicationscited by 161
  7. 2023HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codeccited by 97
  8. 2019High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processorscited by 19
  9. 2023OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workloadcited by 12
  10. 2022Multi-Objective Hardware-Mapping Co-Optimisation for Multi-DNN Workloads on Chiplet-based Acceleratorscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.