# Inference Time Optimization Using BranchyNet Partitioning

arXiv: https://arxiv.org/abs/2005.04099 · HTML: https://mlsanity.com/paper/2005.04099/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 5 of 5)

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- [Early-exit deep neural networks for distorted images: providing an efficient edge offloading](https://mlsanity.com/paper/2108.09343/) (2021)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks](https://mlsanity.com/paper/2504.03052/) (2025)
- [DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits](https://mlsanity.com/paper/2502.17446/) (2025)

## Abstract (arXiv)

Deep Neural Network (DNN) applications with edge computing presents a trade-off between responsiveness and computational resources. On one hand, edge computing can provide high responsiveness deploying computational resources close to end devices, which may be prohibitive for the majority of cloud computing services. On the other hand, DNN inference requires computational power to be executed, which may not be available on edge devices, but a cloud server can provide it. To solve this problem (trade-off), we partition a DNN between edge device and cloud server, which means the first DNN layers are processed at the edge and the other layers at the cloud. This paper proposes an optimal partition of DNN, according to network bandwidth, computational resources of edge and cloud, and parameter inherent to data. Our proposal aims to minimize the inference time, to allow high responsiveness applications. To this end, we show the equivalency between DNN partitioning problem and shortest path problem to find an optimal solution, using Dijkstra's algorithm.