# Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

arXiv: https://arxiv.org/abs/2503.21109 · HTML: https://mlsanity.com/paper/2503.21109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors](https://mlsanity.com/paper/1903.05898/) (2019)
- [Benchmarking of DL Libraries and Models on Mobile Devices](https://mlsanity.com/paper/2202.06512/) (2022)
- [Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads](https://mlsanity.com/paper/2310.11096/) (2023)
- [Automated Pest Detection with DNN on the Edge for Precision Agriculture](https://mlsanity.com/paper/2108.00421/) (2021)

## Built on by (most cited first, 1 of 1)

- [User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling](https://mlsanity.com/paper/2608.11840/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving demand for mobile device support. However, existing mobile inference frameworks often rely on a single processor per model, limiting hardware utilization and causing suboptimal performance and energy efficiency. Expanding DNN accessibility on mobile platforms requires adaptive, resource-efficient solutions to meet rising computational needs without compromising functionality. Parallel inference of multiple DNNs on heterogeneous processors remains challenging. Some works partition DNN operations into subgraphs for parallel execution across processors, but these often create excessive subgraphs based only on hardware compatibility, increasing scheduling complexity and memory overhead. To address this, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy for optimizing multi-DNN inference on mobile heterogeneous processors. ADMS constructs an optimal subgraph partitioning strategy offline, balancing hardware operation support and scheduling granularity, and uses a processor-state-aware algorithm to dynamically adjust workloads based on real-time conditions. This ensures efficient workload distribution and maximizes processor utilization. Experiments show ADMS reduces multi-DNN inference latency by 4.04 times compared to vanilla frameworks.