# Orchestrating Multimodal DNN Workloads in Wireless Neural Processing

arXiv: https://arxiv.org/abs/2603.02109 · HTML: https://mlsanity.com/paper/2603.02109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation](https://mlsanity.com/paper/2209.10382/) (2022)
- [MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores](https://mlsanity.com/paper/2104.13997/) (2021)
- [AI-RAN: Transforming RAN with AI-driven Computing Infrastructure](https://mlsanity.com/paper/2501.09007/) (2025)
- [FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing](https://mlsanity.com/paper/2302.10681/) (2023)
- [Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference](https://mlsanity.com/paper/1907.12475/) (2019)
- [DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads](https://mlsanity.com/paper/2212.03414/) (2022)
- [MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks](https://mlsanity.com/paper/2305.05843/) (2023)
- [Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck](https://mlsanity.com/paper/2405.09514/) (2024)
- [Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads](https://mlsanity.com/paper/2310.11096/) (2023)
- [Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment](https://mlsanity.com/paper/2412.00862/) (2024)

## Abstract (arXiv)

In edge inference, wireless resource allocation and accelerator-level deep neural network (DNN) scheduling have yet to be co-optimized in an end-to-end manner. The lack of coordination between wireless transmission and accelerator-level DNN execution prevents efficient overlap, leading to higher end-to-end inference latency. To address this issue, this paper investigates multimodal DNN workload orchestration in wireless neural processing (WNP), a paradigm that integrates wireless transmission and multi-core accelerator execution into a unified end-to-end pipeline. First, we develop a unified communication-computation model for multimodal DNN execution and formulate the corresponding optimization problem. Second, we propose O-WiN, a framework that orchestrates DNN workloads in WNP through two tightly coupled stages: simulation-based optimization and runtime execution. Third, we develop two algorithms, RTFS and PACS. RTFS schedules communication and computation sequentially, whereas PACS interleaves them to enable pipeline parallelism by overlapping wireless data transfer with accelerator-level DNN execution. Simulation results demonstrate that PACS significantly outperforms RTFS under high modality heterogeneity by better masking wireless latency through communication-computation overlap, thereby highlighting the effectiveness of communication-computation pipelining in accelerating multimodal DNN execution in WNP.