# Scheduling Techniques of AI Models on Modern Heterogeneous Edge GPU -- A Critical Review

arXiv: https://arxiv.org/abs/2506.01377 · HTML: https://mlsanity.com/paper/2506.01377/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2025.

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

- [Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles](https://mlsanity.com/paper/2607.10942/) (2026)
- [Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines](https://mlsanity.com/paper/2608.11770/) (2026)
- [Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection](https://mlsanity.com/paper/2609.34142/) (2026)

## Abstract (arXiv)

In recent years, the development of specialized edge computing devices has significantly increased, driven by the growing demand for AI models. These devices, such as the NVIDIA Jetson series, must efficiently handle increased data processing and storage requirements. However, despite these advancements, there remains a lack of frameworks that automate the optimal execution of optimal execution of deep neural network (DNN). Therefore, efforts have been made to create schedulers that can manage complex data processing needs while ensuring the efficient utilization of all available accelerators within these devices, including the CPU, GPU, deep learning accelerator (DLA), programmable vision accelerator (PVA), and video image compositor (VIC). Such schedulers would maximize the performance of edge computing systems, crucial in resource-constrained environments. This paper aims to comprehensively review the various DNN schedulers implemented on NVIDIA Jetson devices. It examines their methodologies, performance, and effectiveness in addressing the demands of modern AI workloads. By analyzing these schedulers, this review highlights the current state of the research in the field. It identifies future research and development areas, further enhancing edge computing devices' capabilities.