# Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraint

arXiv: https://arxiv.org/abs/2112.11413 · HTML: https://mlsanity.com/paper/2112.11413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [A Deterministic Linear Program Solver in Current Matrix Multiplication Time](https://mlsanity.com/paper/1910.11957/) (2019)
- [Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems](https://mlsanity.com/paper/1604.07525/) (2016)

## Built on by (most cited first, 3 of 3)

- [Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge](https://mlsanity.com/paper/2304.00891/) (2023)
- [Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems](https://mlsanity.com/paper/2402.16904/) (2024)
- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)

## Abstract (arXiv)

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference, we study the problem of offloading inference jobs by considering the following novel aspects: 1) in contrast to a typical computational job, the processing time of an inference job depends on the size of the ML model, and 2) recently proposed Deep Neural Networks (DNNs) for resource-constrained devices provide the choice of scaling the model size. We formulate an assignment problem with the aim of maximizing the total inference accuracy of n data samples available at the ED, subject to a time constraint T on the makespan. We propose an approximation algorithm AMR2, and prove that it results in a makespan at most 2T, and achieves a total accuracy that is lower by a small constant from optimal total accuracy. As proof of concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNet, and is connected to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR2 for image classification application.