终身学习AI加速器的设计原则
Design Principles for Lifelong Learning AI Accelerators
AI总结：
针对终身学习AI落地边缘平台的硬件需求，本文明确了终身学习AI加速器的核心能力与评估指标，梳理现有边缘AI加速器情况，并结合新兴技术探索其未来设计方向。
摘要：
终身学习——即智能体在其整个生命周期内持续学习的能力——是生物学习系统的标志性特征，也是人工智能（AI）领域的核心挑战。终身学习算法的发展有望催生一系列新型AI应用，但这也需要开发配套的硬件加速器，尤其是当模型要部署在有着严格尺寸、重量与功耗约束的边缘平台上时。本文探讨了面向无束缚环境部署的终身学习AI加速器的设计。我们明确了终身学习加速器应具备的关键理想能力，并列出了评估这类加速器的指标。随后我们讨论了当前的边缘AI加速器，并结合不同新兴技术可能发挥的作用，探索了终身学习加速器的未来设计方向。
英文摘要：
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, particularly if the models are to be deployed on edge platforms, which have strict size, weight, and power constraints. Here, we explore the design of lifelong learning AI accelerators that are intended for deployment in untethered environments. We identify key desirable capabilities for lifelong learning accelerators and highlight metrics to evaluate such accelerators. We then discuss current edge AI accelerators and explore the future design of lifelong learning accelerators, considering the role that different emerging technologies could play.
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。
