# Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput

arXiv: https://arxiv.org/abs/2210.12219 · HTML: https://mlsanity.com/paper/2210.12219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2022.

## Built on by (most cited first, 1 of 1)

- [Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears](https://mlsanity.com/paper/2508.17262/) (2025)

## Abstract (arXiv)

Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these clusters while maximizing the inference throughput of the system. We present an algorithm which partitions DNNs and distributes them across a set of edge devices with the goal of minimizing the bottleneck latency and therefore maximizing inference throughput. The system scales well to systems of different node memory capacities and numbers of nodes. We find that we can reduce the bottleneck latency by 10x over a random algorithm and 35% over a greedy joint partitioning-placement algorithm. Furthermore we find empirically that for the set of representative models we tested, the algorithm produces results within 9.2% of the optimal bottleneck latency.