2023/10/27 by Roberto Morabito, Morabito, Roberto, Mallik Tatipamula +5 · 2 citations
Computer Science · Engineering · #Advanced Memory and Neural Computing #Age of Information Optimization #Artificial intelligence #Computer science #Data science #Distributed computing #Diversity (politics) #Domain (mathematical analysis) #Enhanced Data Rates for GSM Evolution #Inference #IoT and Edge/Fog Computing #Operating system #Software #Software engineering
paper · pdf · doi:10.48550/arxiv.2311.03375
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.