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The network edge's role in Artificial Intelligence (AI) inference processingis rapidly expanding, driven by a plethora of applications seekingcomputational 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 AIinference 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 indevice integration and coordination. In this paper, our objectives arethree-fold. Firstly, we outline the requirements and components of a frameworkthat accommodates hardware diversity. Next, we assess the impact of deviceheterogeneity on AI inference performance, identifying strategies to optimizeoutcomes without compromising service quality. Lastly, we shed light on theprevailing challenges and opportunities in this domain, offering insights forboth the research community and industry stakeholders.

Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities