异构受限计算中的边缘AI推理：可行性与机遇
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities
AI总结：
本文针对边缘AI推理中设备异构带来的集成协同难题，梳理了适配硬件多样性的框架需求，提出不降低服务质量的性能优化策略，并明确了该领域的挑战与机遇。
摘要：
在众多追求计算优势的应用推动下，网络边缘在人工智能（AI）推理处理中的作用正迅速扩大。这些应用依托强大的AI能力、优先保障实时响应性，力求实现数据驱动的效率提升。然而，随着需求增长，系统复杂度也随之提升。AI推理加速器的涌现展现了技术创新，但也凸显了诸多挑战，尤其是这些设备的软硬件配置各不相同。这种异构性虽对特定任务有利，却给设备集成与协同带来了障碍。本文的研究目标分为三方面：首先，我们梳理了可适配硬件多样性的框架的需求与组成部分；其次，我们评估了设备异构性对AI推理性能的影响，明确了在不降低服务质量的前提下优化结果的策略；最后，我们阐明了该领域当前面临的挑战与机遇，为研究界和产业界相关从业者提供参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
