# Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time

arXiv: https://arxiv.org/abs/2503.21476 · HTML: https://mlsanity.com/paper/2503.21476/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 27 Mar 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2012.03257/) (2020)
- [Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing](https://mlsanity.com/paper/2007.07122/) (2020)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence](https://mlsanity.com/paper/2007.09072/) (2020)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)
- [DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis](https://mlsanity.com/paper/2502.06295/) (2025)
- [Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2011.02154/) (2020)
- [The Serverless Computing Survey: A Technical Primer for Design Architecture](https://mlsanity.com/paper/2112.12921/) (2021)
- [Understanding Time Variations of DNN Inference in Autonomous Driving](https://mlsanity.com/paper/2209.05487/) (2022)
- [A lightweight design for serverless Function-as-a-Service](https://mlsanity.com/paper/2010.07115/) (2020)

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

- [Joint Memory Frequency and Computing Frequency Scaling for Energy-efficient DNN Inference](https://mlsanity.com/paper/2509.17970/) (2025)
- [Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission](https://mlsanity.com/paper/2601.08135/) (2026)
- [Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems](https://mlsanity.com/paper/2603.02579/) (2026)
- [Joint Optimization of Memory and Computing Frequency for Energy-Efficient DNN Inference](https://mlsanity.com/paper/2608.13863/) (2026)

## Abstract (arXiv)

In edge intelligence systems, deep neural network (DNN) partitioning and data offloading can provide real-time task inference for resource-constrained mobile devices. However, the inference time of DNNs is typically uncertain and cannot be precisely determined in advance, presenting significant challenges in ensuring timely task processing within deadlines. To address the uncertain inference time, we propose a robust optimization scheme to minimize the total energy consumption of mobile devices while meeting task probabilistic deadlines. The scheme only requires the mean and variance information of the inference time, without any prediction methods or distribution functions. The problem is formulated as a mixed-integer nonlinear programming (MINLP) that involves jointly optimizing the DNN model partitioning and the allocation of local CPU/GPU frequencies and uplink bandwidth. To tackle the problem, we first decompose the original problem into two subproblems: resource allocation and DNN model partitioning. Subsequently, the two subproblems with probability constraints are equivalently transformed into deterministic optimization problems using the chance-constrained programming (CCP) method. Finally, the convex optimization technique and the penalty convex-concave procedure (PCCP) technique are employed to obtain the optimal solution of the resource allocation subproblem and a stationary point of the DNN model partitioning subproblem, respectively. The proposed algorithm leverages real-world data from popular hardware platforms and is evaluated on widely used DNN models. Extensive simulations show that our proposed algorithm effectively addresses the inference time uncertainty with probabilistic deadline guarantees while minimizing the energy consumption of mobile devices.