NSF ACED: ROOTS: Real-time Optimization Of Transceiver Systems

NSF ACED：ROOTS：收发器系统的实时优化

• 批准号: 2314813
• 负责人: Jeffrey Walling
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314813&HistoricalAwards=false
• 关键词: NSF; ACED; ROOTS; Real; time

Future wireless systems such as those proposed for the sixth generation of wireless networks (6G) will expand upon our present networking capability and provide for new and emerging services such as augmented/virtual reality, remote surgery, sensing, and imaging of our environment. This will require new wireless circuits and systems that are more precise than those required for previous generations of wireless networks. The goal of this research is to use machine learning to continuously improve the precision and accuracy of wireless circuits and systems. This can enable wireless devices to operate more efficiently and provide more robust wireless connectivity. The proposed investigation will also provide insight into embedding machine learning directly with RF transceiver hardware. This research that is focused on the investigation of CHIPS will result in the design of novel integrated circuit and integration techniques that are promising for 6G. In addition to the scientific outcomes of the investigation, this proposal involves international collaboration between universities in the United States and Taiwan. The educational objectives will cross-train 4 Ph. D. and 4 M.S. students between the partnering universities in both countries. The investigators also plan curriculum development for their undergraduate and graduate courses in circuits and systems design and plan to involve undergraduate students from their courses at earlier stages of their educational development in the research associated with the proposal.The objective of this proposal is to investigate the use of machine learning to continuously calibrate and optimize millimeter wave (mmWave) transceiver hardware. This is warranted because the projections for 6G expand the use of mmWave and near-THz spectrum, which require circuits and systems that can operate flexibly and with better linearity across wider instantaneous bandwidth. Commercially produced transceivers now use 100s-1000s of bits for trimming and calibration; however, many of these trims are only performed at the initial programming of the integrated circuit on automated testing equipment. This creates a large calibration and optimization space that this project will use to investigate continuous background optimizations using a local, efficient neuromorphic compute-in-memory system. As part of the program, highly trimmable digital transmitters will be integrated with a low-power calibration receiver that will be used to estimate transmitter parameters. The outputs of the receiver will be input into a neuromorphic computing accelerator with compute-in-memory (CIM) that will be running calibration/optimization algorithms that control the transmitters trimming and calibration bits. The project has two phases. In the first phase, the transceiver circuits and neuromorphic computing accelerator will be designed separately and characterized. They will be co-packaged for initial investigation of the interface between the systems. In the second phase, the experimental findings from phase one will be used to guide integration in phase two. The phase two demonstration experiment includes a fully integrated system and experiments on system optimizations to improve the system efficiency and linearity. The findings from these experiments will provide relevant information to scale designs for future systems that add complexity including multiple-input, multiple-output systems for wireless beamforming.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.

未来的无线系统，例如为第六代无线网络（6 G）提出的那些系统，将扩展我们目前的联网能力，并提供新的和新兴的服务，例如增强/虚拟现实、远程手术、传感和我们环境的成像。这将需要比前几代无线网络所需的更精确的新无线电路和系统。这项研究的目标是利用机器学习不断提高无线电路和系统的精度和准确性。这可以使无线设备能够更有效地操作并提供更鲁棒的无线连接。拟议的调查还将提供直接与RF收发器硬件嵌入机器学习的见解。这项研究的重点是CHIPS的研究，将设计出对6 G有前途的新型集成电路和集成技术。除了调查的科学成果外，这项提案还涉及美国和台湾大学之间的国际合作。培养目标为交叉培养4名博士生。4 MS两国合作大学之间的学生。研究人员还计划为电路和系统设计的本科生和研究生课程制定课程，并计划让本科生在教育发展的早期阶段参与与该提案相关的研究。该提案的目的是研究使用机器学习来持续校准和优化毫米波（mmWave）收发器硬件。这是有道理的，因为6 G的预测扩展了毫米波和近THz频谱的使用，这需要电路和系统能够灵活地工作，并且在更宽的瞬时带宽上具有更好的线性度。商业生产的收发器现在使用100 - 1000位进行微调和校准;然而，这些微调中的许多仅在自动测试设备上的集成电路的初始编程时执行。这创建了一个大型校准和优化空间，该项目将使用该空间来研究使用本地、高效的神经形态内存计算系统的连续后台优化。作为该计划的一部分，高度可微调的数字发射机将与低功耗校准接收机集成，后者将用于估计发射机参数。接收器的输出将被输入到具有存储器中计算（CIM）的神经形态计算加速器中，该神经形态计算加速器将运行控制发射器微调和校准位的校准/优化算法。该项目分为两个阶段。在第一阶段，收发器电路和神经形态计算加速器将被单独设计和表征。它们将被共同打包，用于对系统之间的接口进行初步调查。在第二阶段，第一阶段的实验结果将用于指导第二阶段的整合。第二阶段的演示实验包括一个完全集成的系统和系统优化实验，以提高系统的效率和线性。这些实验的结果将为未来系统的规模设计提供相关信息，这些系统将增加复杂性，包括用于无线波束形成的多输入多输出系统。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。