Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms

合作研究：SHF：小型：物联网人工智能 (AIoT)：理论、架构和算法

• 批准号: 2221742
• 负责人: Nima Maghari
• 金额: $ 19.06万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221742&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Artificial

The fusion of AI and IoT creates Artificial-Intelligence-of-Things (AIoT), which is expected to not only boost the intelligence on end devices, but also unleash the power of IoT data better and faster. Given the presence of confidential and distributed IoT data in many fields, federated learning has been one promising approach to unlock the potential of AIoT by enabling collaborative intelligence without migrating private end-device data to a central server. However, the heavy burden of state-of-the-art AI on storage and computing resources stands at odds with most IoT hardware platforms that are resource-constrained, which raises daunting challenges when deploying federated intelligence in AIoT. The research team explores hardware-efficient AI techniques to support federated knowledge transfer across diverse IoT hardware platforms to expand the scope of AIoT from theory, architecture, and algorithm perspectives. The proposed research brings tangible benefits to a broad range of disciplines that employ AI and IoT technologies, promoting the fusion of AI and IoT. The project provides training opportunities for undergraduate and graduate students from underrepresented groups. The outreach efforts on AIoT topics and research findings are directed towards K-12 audiences. This project provides the theoretical and empirical evidence to facilitate the deployment of hardware-efficient AI techniques in federated IoT environments, which fills a critical void - the existing approaches fail to address the widespread resource, efficiency, and privacy challenges in AIoT. This project consists of four aspects: (1) enabling hardware-efficient AI from microscope operations, neural quantization, to theoretically guide specialized quantization for federated intelligence across various IoT hardware platforms, (2) exploring another ground-breaking hardware-efficient AI technique, neural architecture pruning, to seek optimal sub-network architectures in a data-agnostic manner, (3) identifying new privacy vulnerabilities and developing defensive mechanisms for the AIoT designs to encourage broad participation, (4) establishing a general-purpose AIoT testbed. Through the architecture-algorithm-hardware co-design, the research intends to unleash the utmost potential of various IoT hardware platforms and federated intelligence to expand the scope of AIoT applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

AI和IoT的融合创造了人工智能（Aiot），这不仅可以增强最终设备上的智能，而且可以更好地释放物联网数据的力量。鉴于在许多领域中存在机密和分布的物联网数据，联合学习一直是一种有前途的方法，可以通过启用协作智能而无需将私人最终设备数据迁移到中央服务器来解锁Aiot的潜力。但是，最先进的AI负担在存储和计算资源上的负担与大多数资源受限的物联网硬件平台矛盾，这在Aiot中部署联邦情报时会带来艰巨的挑战。研究团队探索了硬件有效的AI技术，以支持各种物联网硬件平台上的联合知识转移，以扩大Aiot的范围，从理论，体系结构和算法的角度来看。拟议的研究为采用AI和IoT技术的广泛学科带来了切实的好处，从而促进了AI和IoT的融合。该项目为来自代表性不足的小组的本科和研究生提供了培训机会。关于AIOT主题和研究结果的外展工作针对K-12受众。该项目提供了理论和经验的证据，以促进在联合的物联网环境中的硬件有效AI技术的部署，这填补了关键的空白 - 现有方法无法解决Aiot中广泛的资源，效率和隐私挑战。该项目由四个方面组成：（1）从显微镜操作，神经量化，到理论上指导各种IoT硬件平台联合智能的专门量化的硬件AI，（2）探索另一个具有突破性的硬件AI技术，神经效率的AI技术，神经架构，以寻求最佳的Data Data-Newing subsiganterighatient（3） （4）建立通用Aiot测试台的AIT设计以鼓励广泛参与的脆弱性和防御机制。通过架构 - 阿尔戈里思 - 硬件共同设计，该研究旨在释放各种物联网硬件平台和联合智能的最大潜力，以扩大AIOT应用程序的范围。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务。