# Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence

arXiv: https://arxiv.org/abs/2007.09072 · HTML: https://mlsanity.com/paper/2007.09072/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)
- [Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time](https://mlsanity.com/paper/2503.21476/) (2025)
- [A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices](https://mlsanity.com/paper/2304.10020/) (2023)
- [Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence](https://mlsanity.com/paper/2312.16497/) (2023)
- [Latency-Memory Optimized Splitting of Convolution Neural Networks for Resource Constrained Edge Devices](https://mlsanity.com/paper/2107.09123/) (2021)
- [SmartSplit: Latency-Energy-Memory Optimisation for CNN Splitting on Smartphone Environment](https://mlsanity.com/paper/2111.01077/) (2021)
- [Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms](https://mlsanity.com/paper/2312.06440/) (2023)
- [High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computing](https://mlsanity.com/paper/2312.15850/) (2023)
- [A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence](https://mlsanity.com/paper/2409.16537/) (2024)
- [Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market](https://mlsanity.com/paper/2503.04521/) (2025)

## Abstract (arXiv)

Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence services empowering massive mobile and Internet of Things (IoT) devices with AI capability. With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time. However the resources in each individual edge server are typically limited. Therefore, any resource optimization involving edge servers is by nature a resource-constrained optimization problem and needs to be tackled in such realistic context. Motivated by this observation, we investigate the optimization problem of DNN partitioning (an emerging DNN offloading scheme) in a realistic multi-user resource-constrained condition that rarely considered in previous works. Despite the extremely large solution space, we reveal several properties of this specific optimization problem of joint multi-UE DNN partitioning and computational resource allocation. We propose an algorithm called Iterative Alternating Optimization (IAO) that can achieve the optimal solution in polynomial time. In addition, we present rigorous theoretic analysis of our algorithm in terms of time complexity and performance under realistic estimation error. Moreover, we build a prototype that implements our framework and conduct extensive experiments using realistic DNN models, whose results demonstrate its effectiveness and efficiency.