发表机构
University of Oxford; University of Minho; Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho; Swansea University; University of Pavia; Institut de Ciència de Materials de Barcelona (ICMAB-CSIC); Luxembourg Institute of Science and Technology (LIST); University of Luxembourg; Nanoshuttle; Fondazione Bruno Kessler; University at Buffalo – The State University of New York; IBM Research GmbH – Zürich Research laboratory（牛津大学; 米尼奥大学; 米尼奥大学物质物理与新兴技术实验室; 斯旺西大学; 帕维亚大学; 巴塞罗那材料科学研究所; 卢森堡科学与技术研究院; 卢森堡大学; 纳米穿梭公司; 布鲁诺·凯斯勒基金会; 纽约州立大学布法罗分校; IBM苏黎世研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文展望了利用室内光伏和（反）铁电材料实现边缘计算，通过本地能量收集、存储与神经形态计算协同，解决分布式智能的能源瓶颈，使其成为集中式AI的可持续替代方案。
AI 中文摘要
人工智能（AI）在大型数据中心中的指数级、普及性增长，将考验其基础设施的极限，包括电力和水资源供应，这些资源正变得越来越珍贵。与其在集中式服务器中执行所有AI计算，对于许多功能而言，这些计算可以在由数十亿个小型自主节点组成的网络本地执行，从而节省与通信相关的巨大能源成本。这种替代范式被称为边缘计算或分布式智能（DI），但由于缺乏与计算基础设施能源需求相匹配的可靠本地能源供应，其发展一直受到阻碍。随着高性能室内光伏（IPVs）在本地能量收集方面的快速进展，以及通过神经形态或存内计算设备降低计算成本，解决这一挑战的机会正在涌现。在这篇展望文章中，我们讨论了IPVs对DI的要求、新兴材料在多大程度上满足这些要求，以及当前差距如何能够被解决。我们提出，（反）铁电材料可以超越静电能量存储和低功耗存内神经形态计算两方面的瓶颈。这篇展望的核心主题是，能量收集、存储和计算之间的协同创造对于使DI成为集中式AI的可靠且更可持续的替代方案至关重要。
英文摘要
The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and water supplies, which are becoming increasingly precious resources. Rather than performing all AI computations in centralized servers, for many functionalities, these can be performed locally across a network of billions of small, autonomous nodes, saving substantial energy costs associated with communication. This alternative paradigm is known as edge computing, or distributed intelligence (DI), but has been held back by the lack of availability of reliable local energy supplies matching the energy requirements of the computational infrastructure. Opportunities to address this challenge are emerging with rapid advances in high-performance indoor photovoltaics (IPVs) for local energy harvesting, as well as reductions in computational cost through neuromorphic or in-memory computing devices. In this perspective, we discuss the requirements of IPVs for DI, the extent to which emerging materials fulfil these requirements, and how current gaps could be addressed. We make the case that (anti)ferroelectric materials can surpass bottlenecks for both electrostatic energy storage and low-power in-memory neuromorphic computing. The core theme of this perspective is that co-creation between energy harvesting, storage and computing is essential for making DI a reliable and more sustainable alternative to centralized AI.
Comments66 pages main text, 50 pages SI. 3 figures, 3 boxes in the main text
