# MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks

arXiv: https://arxiv.org/abs/2609.14660 · HTML: https://mlsanity.com/paper/2609.14660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022)
- [Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)](https://mlsanity.com/paper/2204.05223/) (2022)
- [Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference](https://mlsanity.com/paper/2105.13618/) (2021)
- [DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device](https://mlsanity.com/paper/2104.09949/) (2021)
- [Dynamic Split Computing for Efficient Deep Edge Intelligence](https://mlsanity.com/paper/2205.11269/) (2022)
- [SC2 Benchmark: Supervised Compression for Split Computing](https://mlsanity.com/paper/2203.08875/) (2022)
- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)
- [Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning](https://mlsanity.com/paper/2102.02638/) (2021)
- [DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices](https://mlsanity.com/paper/2202.01699/) (2022)
- [Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs](https://mlsanity.com/paper/2209.13443/) (2022)
- [A Multi-task Supervised Compression Model for Split Computing](https://mlsanity.com/paper/2501.01420/) (2025)
- [MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge](https://mlsanity.com/paper/2306.12830/) (2023)
- [Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers](https://mlsanity.com/paper/2503.04148/) (2025)
- [Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Framework for Server-Light DNN Inference over Massively Distributed Clients via Training-Free Intermediate Feature Compression](https://mlsanity.com/paper/2511.11608/) (2025)

## Abstract (arXiv)

Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge server, leveraging the growing computational capabilities of modern System-on-Chips while alleviating server load. As intelligent indoor environments such as smart offices grow increasingly populated with diverse IoT devices, a single edge server must simultaneously assist multiple devices, each competing for the same shared inference resources. Without a principled mechanism to manage this shared load, the server is quickly overwhelmed, causing latency SLO violations and rendering server-assisted inference ineffective. In this work, we present MANE, a distributed inference framework that equips the server with a multi-path tail architecture, enabling a dynamic accuracy--throughput trade-off at runtime. By introducing a novel multi-path model architecture, a three-stage training scheme featuring a Joint Head Network Distillation loss and a hysteresis-based scheduler with an equitable device-fallback policy, MANE maintains over 80% SLO satisfaction rate where state-of-the-art onloading methods fail completely, while preserving accuracy 6pp higher than on-device alternatives, across up to 40 concurrent devices.