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）当前需要访问Internet和非常大而复杂的远程计算机来做出决策和预测。这会导致长期延误，隐私和安全问题。 AI中的最新技术（称为“ Edge AI”）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。但是，Edge AI仍处于起步阶段，并且需要解决一些重要的技术问题。这项研究基础设施改进Track-2以EPSCOR合作（RII Track-2 FEC）奖是六所大学（包括两家少数派服务机构）与阿拉巴马州，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。作为对项目的新技术的测试，项目团队将通过监测患者自己的呼吸来预测糖尿病的发作，而无需医生来解释结果，以预测糖尿病的发作。它将为高级大学生提供研究培训机会，还将在课程中培训高中老师，以教育自己的学生，以Edge AI的原则来了解未来的美国劳动力，以这些基本的概念为明天的世界中的这些基本概念。团队将要开发的基本贡献和技术创新包括：（i）边缘平台的轻量级AI-Empoperapity推理和机器学习算法； （ii）一种新的针对特定应用的集成电路（ASIC）设计方法，以使具有超低功率，可重构性和短期开发周期的AI ASIC； （iii）基于新型功能化的纳米尺度传感器材料，具有纳米3D打印技术的传感器设备平台； （iv）Edge AI设备平台利用先前的进步以满足不同用例的要求。基于开发的基础架构，针对糖尿病护理的用例，团队将设计，原型和测试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使大幅降低成本和高功率效率。领先的机构是南阿拉巴马大学；合作机构是北达科他州立大学，阿肯色大学，北达科他大学，阿拉巴马州农工大学和Nueta Hidatsa Sahnish学院。该团队将与多个行业合作伙伴紧密合作，以在不同的用例中采用和适应已发达的Edge AI基础架构。该项目的研究成果将加速Edge AI的发展，并将提高美国在AI中的竞争力。此外，该项目将整合研究，教育和劳动力发展，以便在多个层面上提供有效的培训。该项目将开发一条从高中到本科，研究生，博士后培训，初级教师和行业从业人员的教育管道。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来评估通过评估而被认为是珍贵的。

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