Enabling Adaptive Voltage Regulation: Control, Machine Learning, and Circuit Design

实现自适应电压调节：控制、机器学习和电路设计

• 批准号: 2000851
• 负责人: Peng Li
• 金额: $ 30.42万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000851&HistoricalAwards=false
• 关键词: Enabling; Adaptive; Voltage; Regulation; Control

Supply voltage regulation serves the critical role of delivering power to on-chip devices at well-regulated voltage levels. Voltage regulation presents key design challenges of electronic systems ranging from high-performance microprocessors to mobile system-on-a-chips. In such systems, the ever-growing need for processing capability must be fulfilled while staying within specified power, thermal, and battery-life limits. Power must be managed and delivered while maximizing system power efficiency in every possible way. The proposed research aims to address the above voltage regulation challenges by taking an interdisciplinary approach. Innovations in control, machine learning, and circuit design will be developed to enable adaptive supply voltage regulation systems involving a variety of on-chip/off-chip voltage regulators. The expected outcomes of this project will help build new generations of highly efficient circuits and systems that can self-adapt to varying operating conditions. The synergies between circuit/system design, control-theoretical exploration, and machine learning as pursued in this project will promote a new interdisciplinary direction for advancing electronic system design. The depth and breadth of this research will expose students to outstanding educational and training opportunities. Participation from undergraduate and underrepresented students is an important education mission of this project and will be promoted through recruiting and outreaching. The anticipated results from this project are expected to be broad and will be widely disseminated as well as brought to classroom to benefit undergraduate and graduate curriculum. Collaboration and interaction with industry constitutes an important channel for this project to impact the real world, which will be actively pursued. This project is based on the vision that the ultimate quality and efficiency in supply voltage regulation may be best achieved via a heterogeneous chain of voltage processing starting from on-board switching voltage regulators (VRs), to in-package/on-chip switching VRs, and finally to networks of distributed on-chip linear VRs. Heterogeneous voltage regulation (HVR) systems are promising as they encompass regulators with complimentary tradeoffs in response time, size, efficiency, and cost. The ultimate aim of this project is to enable HVR systems that will guarantee power integrity, incur minimal power loss, and autonomously adapt to workload changes and system/environmental uncertainties at multiple temporal scales. The above goal will be achieved by pursuing an integrated solution of novel control theory, circuits, and machine-learning enabled autonomous adaptation. Rigorous design techniques for decentralized and centralized control will be developed for distributed on-chip linear regulator networks and the HVR system with guaranteed stability and regulation performance. Efficient machine-learning algorithms and their on-chip integration will be employed to provide accurate real-time prediction of time-varying load currents. Autonomous adaptation of the HVR system will be supported by power-efficient control policies that preemptively adapt on-chip linear regulator networks and on-chip/off-chip VRs based on machine-learning predicted future current loads. Coping with system uncertainties is another key objective and will be achieved via deployment of control policies that are self-tuned by machine learning to attain the optimal power efficiency. The project will explore system-level design optimization to jointly optimize regulation performance, power efficiency, and design overhead across all voltage processing stages in a HVR system.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.

供应电压调节在良好调节的电压水平上向片上设备传递电力的关键作用。电压法规提出了从高性能微处理器到移动系统芯片的电子系统的关键设计挑战。在这样的系统中，必须在保持指定功率，热力和电池寿命限制内时满足加工能力的不断增长的需求。 必须以各种可能的方式管理和传递功率，同时最大化系统功率效率。拟议的研究旨在通过采用跨学科方法来应对上述电压调节挑战。 将开发控制，机器学习和电路设计方面的创新，以启用涉及各种芯片/芯片电压调节器的自适应电源调节系统。该项目的预期结果将有助于建立新一代高效的电路和系统，这些电路和系统可以自适应为不同的操作条件。该项目中所述的电路/系统设计，控制理论探索和机器学习之间的协同作用将促进推进电子系统设计的新跨学科方向。 这项研究的深度和广度将使学生获得出色的教育和培训机会。本科生和代表性不足的学生的参与是该项目的重要教育任务，将通过招聘和推广来促进。该项目的预期结果预计将是广泛的，并且将被广泛传播，并将其带到教室，以使本科和研究生课程受益。 与行业的合作和互动是该项目影响现实世界的重要渠道，这将被积极追求。 该项目基于这样的愿景：供应电压调节的最终质量和效率可以通过从机上开关电压调节器（VRS）到包装/芯片上的芯片开关VR，最终到分布式芯片线性线性VR的网络从车载开关电压调节器（VRS）开始，从而最好地实现供应电压调节的最终质量和效率。 异构电压调节（HVR）系统在响应时间，尺寸，效率和成本方面具有免费权衡的监管机构，因此很有希望。该项目的最终目的是启用HVR系统，以确保功率完整性，最小的功率损失以及自主性地适应工作负载的变化以及在多个时间尺度上的系统/环境不确定性。 上述目标将通过追求新颖的控制理论，电路和机器学习能力自主适应的综合解决方案来实现。将开发用于分散和集中控制的严格设计技术，用于分布式芯片线性调节器网络和具有保证稳定性和调节性能的HVR系统。 有效的机器学习算法及其片上集成将用于提供准确的实时预测时间变化的负载电流。 HVR系统的自主改编将由发电的控制策略提供支持，这些策略可以根据机器学习预测的未来当前负载来促进芯片上线性调节器网络和芯片/Off-Chip VRS的支持。 应对系统不确定性是另一个关键目标，可以通过部署通过机器学习进行自调整以达到最佳功率效率的控制策略来实现。 该项目将探索系统级设计优化，以共同优化HVR系统中所有电压处理阶段的监管性能，功率效率和设计间接费用。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来通过评估来获得支持的。

项目成果

Variation-Aware Heterogeneous Voltage Regulation for Multi-Core Systems-on-a-Chip with On-Chip Machine Learning

具有片上机器学习功能的多核片上系统的变化感知异构电压调节

• DOI: 10.1109/isqed48828.2020.9136985
• 发表时间: 2020
• 期刊: 2020 21st International Symposium on Quality Electronic Design (ISQED
• 作者: Riad, Joseph;Chen, Jianhao;Sanchez-Sinencio, Edgar;Li, Peng
• 通讯作者: Li, Peng

Power Management for Multicore Processors via Heterogeneous Voltage Regulation and Machine Learning Enabled Adaptation

• DOI: 10.1109/tvlsi.2019.2923911
• 发表时间: 2019-07
• 期刊: IEEE Transactions on Very Large Scale Integration (VLSI) Systems
• 影响因子: 2.8
• 作者: Xin Zhan;Jianhao Chen;E. Sánchez-Sinencio;Peng Li

A Stabilizing Centralized Controller for On-Chip Power Delivery Networks

用于片上供电网络的稳定集中控制器

• DOI: 10.1109/tcsii.2019.2926670
• 发表时间: 2020
• 期刊: IEEE Transactions on Circuits and Systems II: Express Briefs
• 作者: Riad, Joseph;Li, Peng;Sanchez-Sinencio, Edgar
• 通讯作者: Sanchez-Sinencio, Edgar

Parallel Time Batching: Systolic-Array Acceleration of Sparse Spiking Neural Computation

• DOI: 10.1109/hpca53966.2022.00031
• 发表时间: 2022-01-01
• 期刊: 2022 IEEE INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE (HPCA 2022)
• 作者: Lee, Jeong-Jun;Zhang, Wenrui;Li, Peng
• 通讯作者: Li, Peng

Systolic-Array Spiking Neural Accelerators with Dynamic Heterogeneous Voltage Regulation

• DOI: 10.1109/ijcnn52387.2021.9534037
• 发表时间: 2021-07
• 期刊: 2021 International Joint Conference on Neural Networks (IJCNN)
• 作者: Jeong-Jun Lee;Jianhao Chen;Wenrui Zhang;Peng Li