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（例如第六代无线网络（6G））将扩展到我们当前的网络功能，并提供新的和新兴的服务，例如增强/虚拟现实，远程手术，感应和对环境的成像。这将需要新的无线电路和系统，这些电路和系统比前几代无线网络所需的电路更精确。这项研究的目的是使用机器学习不断提高无线电路和系统的精度和准确性。这可以使无线设备更有效地运行并提供更强大的无线连接。拟议的调查还将直接与RF收发器硬件直接嵌入机器学习。这项侧重于研究芯片的研究将导致设计新颖的集成电路和集成技术，这对于6G来说是有希望的。除了调查的科学成果外，该提案还涉及美国和台湾大学之间的国际合作。教育目标将交叉训练4Ph。D.和4 M.S.两国合作大学之间的学生。调查人员还计划在电路和系统设计中的本科和研究生课程的课程开发，并计划与该建议有关的研究中的教育发展的早期阶段与本科生参与。该建议的目的是调查Maginer Learning的使用，以调查Maginer学习的使用，以持续校准和优化毫米毫米波动（MMMMWAVE）。之所以需要这是因为6G的预测扩展了MMWave和近THZ频谱的使用，这些频谱需要电路和系统，这些电路和系统可以灵活地操作，并且在更广泛的瞬时带宽上具有更好的线性性。现在，商业生产的收发器使用100s-1000s的钻头进行修剪和校准；但是，这些装饰中的许多仅在自动测试设备的集成电路的初始编程中进行。这会创建一个较大的校准和优化空间，该项目将使用局部，有效的神经形态计算系统来研究连续的背景优化。作为程序的一部分，高度可疑的数字发射器将与低功率校准接收器集成，该接收器将用于估计发射机参数。接收器的输出将被输入使用具有计算机中的计算（CIM）的神经形态计算加速器，该计算将运行校准/优化算法，以控制发射机进行修剪和校准位。该项目有两个阶段。在第一阶段，将分别设计和表征收发器电路和神经形态计算加速器。它们将共同包装以初步研究系统之间的接口。在第二阶段，第一阶段的实验发现将用于指导第二阶段的集成。第二阶段的演示实验包括完全集成的系统和实验系统优化，以提高系统效率和线性性。这些实验的发现将为未来系统的规模设计提供相关信息，这些信息增加了复杂性，包括多输入，多输出的无线束缚系统。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来进行评估的。