# AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments

arXiv: https://arxiv.org/abs/2504.00407 · HTML: https://mlsanity.com/paper/2504.00407/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2025.

## Built on by (most cited first, 6 of 6)

- [Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments](https://mlsanity.com/paper/2512.22149/) (2025)
- [When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control](https://mlsanity.com/paper/2605.26418/) (2026)
- [Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI](https://mlsanity.com/paper/2512.01039/) (2025)
- [Serverless GPU Architecture for Enterprise HR Analytics: A Production-Scale BDaaS Implementation](https://mlsanity.com/paper/2510.19689/) (2025)
- [CarbonEdge: Carbon-Aware Deep Learning Inference Framework for Sustainable Edge Computing](https://mlsanity.com/paper/2603.27420/) (2026)
- [Winning by Peeking: Unenforced Budgets and Test-Set Selection Inflate Short-Budget AutoML Comparisons](https://mlsanity.com/paper/2608.07303/) (2026)

## Abstract (arXiv)

Edge computing facilitates deep learning in resource-constrained environments, but challenges such as resource heterogeneity and dynamic constraints persist. This paper introduces AMP4EC, an Adaptive Model Partitioning framework designed to optimize deep learning inference in edge environments through real-time resource monitoring, dynamic model partitioning, and adaptive task scheduling. AMP4EC features a resource-aware model partitioner that splits deep learning models based on device capabilities, a task scheduler that ensures efficient load balancing using a weighted scoring mechanism, and a Docker-based deployment environment for validation. Experimental results show up to a 78% reduction in latency and a 414% improvement in throughput compared to baseline methods. The framework achieves consistent performance with low scheduling overhead across varying resource profiles, demonstrating adaptability in high-resource (1 CPU, 1GB RAM) and low-resource (0.4 CPU, 512MB RAM) scenarios. These results highlight AMP4EC's scalability, efficiency, and robustness for real-world edge deployments, addressing the critical need for efficient distributed inference in dynamic, resource-constrained environments.