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

arXiv: https://arxiv.org/abs/2411.17867 · HTML: https://mlsanity.com/paper/2411.17867/

## 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 (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec](https://mlsanity.com/paper/2305.02765/) (2023)
- [High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors](https://mlsanity.com/paper/1903.05898/) (2019)
- [OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload](https://mlsanity.com/paper/2307.03290/) (2023)
- [Multi-Objective Hardware-Mapping Co-Optimisation for Multi-DNN Workloads on Chiplet-based Accelerators](https://mlsanity.com/paper/2210.14657/) (2022)

## Built on by (most cited first, 4 of 4)

- [Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers](https://mlsanity.com/paper/2503.04148/) (2025)
- [Leveraging Simultaneous Usage of Edge GPU Hardware Engines for Video Face Detection and Recognition](https://mlsanity.com/paper/2505.04502/) (2025)
- [Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platforms](https://mlsanity.com/paper/2507.00491/) (2025)
- [Multi-DNN Inference of Sparse Models on Edge SoCs](https://mlsanity.com/paper/2603.09642/) (2026)

## 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.