# Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities

arXiv: https://arxiv.org/abs/2311.03375 · HTML: https://mlsanity.com/paper/2311.03375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Synchromesh: Reliable code generation from pre-trained language models](https://mlsanity.com/paper/2201.11227/) (2022)
- [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019)
- [Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions](https://mlsanity.com/paper/2012.06992/) (2020)
- [Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks](https://mlsanity.com/paper/2202.11098/) (2022)

## Built on by (most cited first, 2 of 2)

- [Q-KVComm: Efficient Multi-Agent Communication Via Adaptive KV Cache Compression](https://mlsanity.com/paper/2512.17914/) (2025)
- [A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration](https://mlsanity.com/paper/2608.28652/) (2026)

## Abstract (arXiv)

The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages. These applications strive for data-driven efficiency, leveraging robust AI capabilities and prioritizing real-time responsiveness. However, as demand grows, so does system complexity. The proliferation of AI inference accelerators showcases innovation but also underscores challenges, particularly the varied software and hardware configurations of these devices. This diversity, while advantageous for certain tasks, introduces hurdles in device integration and coordination. In this paper, our objectives are three-fold. Firstly, we outline the requirements and components of a framework that accommodates hardware diversity. Next, we assess the impact of device heterogeneity on AI inference performance, identifying strategies to optimize outcomes without compromising service quality. Lastly, we shed light on the prevailing challenges and opportunities in this domain, offering insights for both the research community and industry stakeholders.