RII Track 2 FEC: Building Research Infrastructure and Workforce in Edge Artificial Intelligence

RII Track 2 FEC：建设边缘人工智能研究基础设施和劳动力

• 批准号: 2218046
• 负责人: Na Gong
• 金额: $ 600万
• 依托单位: University of South Alabama
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218046&HistoricalAwards=false
• 关键词: RII; Track; FEC; Building; Research

Using Artificial Intelligence (AI) currently requires access to the internet and very large and complex remote computers for making decisions and predictions. This causes long delays and privacy and security concerns. The latest techniques in AI, known as “Edge AI”, avoid these problems by collecting and analyzing data directly on cameras, smart phones, and wearable devices. However, Edge AI is still in its infancy and there are several important technical problems that need to be solved. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award is a collaboration between six universities (including two minority-serving institutions) and several private-sector partners in Alabama, Arkansas, and North Dakota. As a test of the project's new technology, the project team will build a smart wearable device to predict the onset of diabetes by monitoring a patient's own breath without the need for a doctor to interpret the results. It will provide research training opportunities for advanced college students and will also train high-school teachers in lessons to educate their own students in the principles of Edge AI to seed the future US workforce in these essential concepts for tomorrow’s world.The goal of this RII Track-2 FEC award is to develop integrated research infrastructure and workforce in Edge AI. Fundamental contributions and technical innovations to be developed by the team include: (i) light-weight AI-empowered reasoning and machine learning algorithms for edge platforms; (ii) a new Application-Specific Integrated Circuits (ASIC) design methodology to enable AI ASICs with ultra-low power, reconfigurability, and short development cycles; (iii) a sensor device platform for Edge AI based on novel functionalized nano-scaled sensing materials with nano-3D printing techniques; and (iv) an Edge AI device platform exploiting the previous advances to meet the requirements of different use cases. Based on the developed infrastructure, targeting the use case of diabetes care, the team will design, prototype, and test a low-cost smart wearable device for personalized diabetes management. The developed wearable diabetes device will enable significant cost reduction and high power efficiency compared to existing techniques. The leading institution is the University of South Alabama; the collaborating institutions are North Dakota State University, the University of Arkansas, the University of North Dakota, Alabama A&M University, and Nueta Hidatsa Sahnish College. The team will work closely with multiple industry partners to adopt and adapt the developed Edge AI infrastructure in different use cases. Research outcomes of this project will accelerate the development of Edge AI and will increase the competitiveness of the United States in AI. Also, this project will integrate research, education, and workforce development in order to provide effective training at multiple levels. The project will develop an Education-to-Workforce Pipeline from high school to undergraduate, graduate, Post-Doctoral training, junior faculty, and industry practitioners.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.

使用人工智能（AI）目前需要访问互联网和非常大型和复杂的远程计算机来进行决策和预测。这会导致长时间的延迟以及隐私和安全问题。人工智能的最新技术，被称为“边缘人工智能”，通过直接在相机，智能手机和可穿戴设备上收集和分析数据来避免这些问题。然而，Edge AI仍处于起步阶段，有几个重要的技术问题需要解决。这个研究基础设施改善轨道-2重点EPSCoR合作（RII轨道-2 FEC）奖是六所大学（包括两个少数民族服务机构）和亚拉巴马，阿肯色州和北达科他州的几个私营部门合作伙伴之间的合作。作为对该项目新技术的测试，项目团队将构建一款智能可穿戴设备，通过监测患者自己的呼吸来预测糖尿病的发作，而无需医生解释结果。它将为高等院校的学生提供研究培训机会，并将培训高中教师，让他们的学生了解Edge AI的原则，为未来的美国劳动力培养这些未来世界的基本概念。这个RII Track-2 FEC奖项的目标是发展Edge AI的综合研究基础设施和劳动力。该团队将开发的基本贡献和技术创新包括：（i）用于边缘平台的轻量级AI授权推理和机器学习算法;（ii）新的专用集成电路（ASIC）设计方法，使AI ASIC具有超低功耗，可重构性和短开发周期;（iii）基于纳米3D打印技术的新型功能化纳米传感材料的Edge AI传感器设备平台;以及（iv）利用先前的进步来满足不同用例的要求的边缘AI设备平台。基于开发的基础设施，针对糖尿病护理的用例，该团队将设计，原型和测试一种低成本的智能可穿戴设备，用于个性化糖尿病管理。与现有技术相比，开发的可穿戴糖尿病设备将显着降低成本并提高功率效率。主导机构是南亚拉巴马大学;合作机构是北达科他州他州立大学、阿肯色州大学、北达科他州大学、亚拉巴马A M大学和Nueta Hidatsa Sahnish学院。该团队将与多个行业合作伙伴密切合作，在不同的用例中采用和调整已开发的Edge AI基础设施。该项目的研究成果将加速边缘人工智能的发展，并将提高美国在人工智能领域的竞争力。此外，该项目将整合研究，教育和劳动力发展，以提供多层次的有效培训。该项目将开发从高中到本科、研究生、博士后培训、初级教师和行业从业者的教育到劳动力管道。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Blockchain-Based Personal Health Knowledge Graph for Secure Integrated Health Data Management

• DOI: 10.1109/iscc58397.2023.10218032
• 发表时间: 2023-07
• 期刊: 2023 IEEE Symposium on Computers and Communications (ISCC)
• 作者: Juan Li;Vikram Pandey;Rasha Hendawi

PAWN: Programmed Analog Weights for Non-Linearity Optimization in Memristor-Based Neuromorphic Computing System

• DOI: 10.1109/jetcas.2023.3235658
• 发表时间: 2023-03
• 期刊: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
• 影响因子: 4.6
• 作者: Saleh Ahmad Khan;Md. Oli-Uz-Zaman;Jinhui Wang

Stuck-at-Fault Immunity Enhancement of Memristor-Based Edge AI Systems

• DOI: 10.1109/jetcas.2022.3207687
• 发表时间: 2022-12
• 期刊: IEEE Journal on Emerging and Selected Topics in Circuits and Systems
• 影响因子: 4.6
• 作者: Md. Oli-Uz-Zaman;Saleh Ahmad Khan;W. Oswald;Zhiheng Liao;Jinhui Wang

Approximate Memory for Low-Power Video Applications

低功耗视频应用的近似内存

• DOI: 10.1109/access.2023.3283409
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Das, H.;Haidous, A. A.;Smith, S. C.;Gong, N.
• 通讯作者: Gong, N.

Polyaniline-based sensor for real-time plant growth monitoring

用于实时植物生长监测的聚苯胺传感器

• DOI: 10.1016/j.sna.2023.114319
• 发表时间: 2023
• 期刊: Sensors and Actuators A: Physical
• 作者: Borode, Temitope;Wang, Danling;Prasad, Anamika
• 通讯作者: Prasad, Anamika