SHF:Small:Enhancing Performance and Reliability in On-Chip Power Distribution Networks

项目来源

美国国家科学基金(NSF)

项目主持人

Sachin S Sapatnekar

项目受资助机构

UNIVERSITY OF MINNESOTA TWIN CITIES

财政年度

2025,2024

立项时间

未公开

项目编号

2437795

项目级别

国家级

研究期限

未知 / 未知

受资助金额

1200000.00美元

学科

未公开

学科代码

未公开

基金类别

Standard Grant

关键词

Software&Hardware Foundation ; SMALL PROJECT ; DES AUTO FOR MICRO&NANO SYST

参与者

未公开

参与机构

REGENTS OF THE UNIVERSITY OF MINNESOTA

项目标书摘要:Today’s world is increasingly driven by advanced electronic integrated circuits,or chips,which power artificial intelligence(AI),infrastructure,mobile communications,scientific computation,and consumer electronics.The computations in a chip are facilitated by supply voltages that are distributed to the computational elements through power distribution networks(PDNs).These PDNs consist of structures that carry large currents through lossy filamentous wires,which may be inadequate for ensuring robust voltage levels at the computational elements.These effects can potentially lead to incorrect computations and unacceptable errors.Moreover,high currents can cause accelerated aging in the PDN due to phenomena such as electromigration(EM)that can result in chip failure.Developing optimization strategies that ensure supply voltage integrity and PDN reliability is therefore critical.The task is further complicated with the challenges associated with emerging methods for building advanced integrated circuits,including new wire and transistor structures,and elevated on-chip currents and temperatures that degrade performance and reliability.Therefore,the development of PDN design techniques is vital for enabling the next generation of chips that drive computation from the datacenter to the edge.This project aims to address the challenges of optimizing voltage drop and EM in PDNs,while ensuring minimal utilization of the limited available on-chip wiring resources.For the problem of voltage drop,the work will address today’s widely used front-side interconnects as well as newer backside interconnect technologies.Degradations in supply voltages can also cause circuit delays to deteriorate:the interplay of PDN optimization on circuit timing in digital circuits will be addressed in this project through new approaches that break down the barrier between PDN design and timing optimization,performing integrated optimizations that benefit both PDN resource usage and logic circuit metrics.To address EM,this research will advance the use of physics-based approaches that predict chip lifetimes by directly modeling EM-induced stress in interconnects.New methods will be developed based on a stress-electrical duality that maps accurate physics-based stress analysis to methods for analyzing resistor-capacitor(RC)networks and transmission lines.An educational component of the project aims to attract fresh talent to advance the mission of workforce development in the field of electronic design automation.Specific efforts will target students at the K-12,undergraduate,and graduate levels,and intend to attract women and other underrepresented minority groups.Outreach activities will be centered around artificial intelligence and semiconductor technologies,and will be supplemented by curriculum development efforts.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.

人员信息

Sachin S Sapatnekar(Principal Investigator):sachin@umn.edu;

机构信息

【University of Minnesota-Twin Cities(Performance Institution)】StreetAddress:200 Union Street SE,Minneapolis,Minnesota,United States/ZipCode:554550160;【REGENTS OF THE UNIVERSITY OF MINNESOTA】StreetAddress:2221 UNIVERSITY AVE SE STE 100,MINNEAPOLIS,Minnesota,United States/PhoneNumber:6126245599/ZipCode:554143074;

项目主管部门

Directorate for Computer and Information Science and Engineering(CSE)-Division of Computing and Communication Foundations(CCF)

项目官员

Sankar Basu(Email:sabasu@nsf.gov;Phone:7032927843)

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  • 1.Optimal Selection and Placement of Voltage Regulators in 2.5D Heterogeneously Integrated Systems

    • 关键词:
    • Codes (symbols);Electric power transmission;Integer linear programming;Integer programming;Integrated control;Mixed-integer linear programming;Capacitor voltages;Chiplet;Heterogeneous integration;Integer Linear Programming;Integrated systems;Key parts;Mixed integer linear;Power delivery;Switched capacitor;Voltage regulator's
    • Zhang, Hangyu;Yogi, Divya;Harjani, Ramesh;Sapatnekar, Sachin S.
    • 《44th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2025》
    • 2025年
    • October 26, 2025 - October 30, 2025
    • Munich, Germany
    • 会议

    Power delivery is a significant bottleneck in 2.5D heterogeneously integrated (HI) systems. A key part of the solution lies in the use of voltage regulators that distribute power to the chiplets with low transmission losses, thus ensuring supply levels that meet design specifications. Early HI systems have used manual approaches for placement, but as systems grow more complex and employ heterogeneous chiplets with nonuniform power distributions, the solution becomes nontrivial, and automation is essential. This paper addresses the problem of voltage regulator selection and placement for early-stage power planning in HI systems under DC estimates of chiplet loads. It demonstrates an HI technology using an active silicon interposer with embedded switched-capacitor voltage regulators (SCVRs). The problem is formulated as a 0-1 mixed integer linear program (MILP) and determines the optimal number, location, and types of SCVRs. A key part of the solution is using a macromodeling approach to control the size of the MILP. Experimental results across four test cases - covering both homogeneous and heterogeneous chiplet configurations - demonstrate that the method can efficiently find optimal solutions. The tested scenarios span a wide range of power densities, from 0.8 to 4.0W/mm2, and include total load currents ranging from 800A to 1200A. In all cases, the method achieves high accuracy (average error below 1.7%) and completes the optimization within practical runtimes, ranging from 18.8 to 46.1 minutes. © 2025 IEEE.

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