World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
56
Citations
12549
World Ranking
2065
National Ranking
810

Overview

Gu-Yeon Wei is affiliated with Harvard University in the United States. Their research spans the fields of Computer Science and Engineering, with a focus on several key subfields including Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture, Computer Vision and Pattern Recognition, and Computer Networks and Communications.

Their scientific output covers a range of main topics, particularly in Parallel Computing and Optimization Techniques, Advanced Neural Network Applications, Ferroelectric and Negative Capacitance Devices, Embedded Systems Design Techniques, Advanced Memory and Neural Computing, Stochastic Gradient Optimization Techniques, and Green IT and Sustainability.

Gu-Yeon Wei has published extensively, with a presence in multiple respected publication venues. Frequent venues for their work include:

  • arXiv (Cornell University)
  • IEEE Micro
  • IEEE Journal of Solid-State Circuits
  • ACM Transactions on Embedded Computing Systems
  • ACM Transactions on Architecture and Code Optimization

Among their recent papers are:

  • "MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance" (2020, IEEE Micro)
  • "Chasing Carbon: The Elusive Environmental Footprint of Computing" (2022, IEEE Micro)
  • "SMAUG" (2020, ACM Transactions on Architecture and Code Optimization)
  • "DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference" (2020, arXiv, Cornell University)
  • "The Sky Is Not the Limit: A Visual Performance Model for Cyber-Physical Co-Design in Autonomous Machines" (2020, IEEE Computer Architecture Letters)

Their frequent coauthors include:

  • David Brooks
  • David J. Brooks
  • Carole-Jean Wu
  • Udit Gupta
  • Tianyu Jia

The volume of publications and frequent collaboration with recognized researchers reflects a sustained engagement with evolving challenges in computing, energy efficiency, and machine learning systems. Gu-Yeon Wei's work integrates aspects of hardware design and software optimization to address system-level performance and sustainability issues.

Best Publications

  • System level analysis of fast, per-core DVFS using on-chip switching regulators

    Wonyoung Kim;M.S. Gupta;Gu-Yeon Wei;D. Brooks

  • Minerva: enabling low-power, highly-accurate deep neural network accelerators

    Brandon Reagen;Paul Whatmough;Robert Adolf;Saketh Rama

  • Thread motion: fine-grained power management for multi-core systems

    Krishna K. Rangan;Gu-Yeon Wei;David Brooks

  • A Fully-Integrated 3-Level DC-DC Converter for Nanosecond-Scale DVFS

    Wonyoung Kim;D. Brooks;Gu-Yeon Wei

  • MachSuite: Benchmarks for accelerator design and customized architectures

    Brandon Reagen;Robert Adolf;Yakun Sophia Shao;Gu-Yeon Wei

  • Aladdin: a Pre-RTL, power-performance accelerator simulator enabling large design space exploration of customized architectures

    Yakun Sophia Shao;Brandon Reagen;Gu-Yeon Wei;David Brooks

  • Driving high voltage piezoelectric actuators in microrobotic applications

    Michael Karpelson;Gu-Yeon Wei;Robert J. Wood

  • Chasing Carbon: The Elusive Environmental Footprint of Computing

    Udit Gupta;Young Geun Kim;Sylvia Lee;Jordan Tse

  • A fully digital, energy-efficient, adaptive power-supply regulator

    Gu-Yeon Wei;M. Horowitz

  • A portable, low-power, wireless two-lead EKG system

    T.R.F. Fulford-Jones;Gu-Yeon Wei;M. Welsh

  • Understanding voltage variations in chip multiprocessors using a distributed power-delivery network

    Meeta S. Gupta;Jarod L. Oatley;Russ Joseph;Gu-Yeon Wei

  • An Ultra Low Power System Architecture for Sensor Network Applications

    Mark Hempstead;Nikhil Tripathi;Patrick Mauro;Gu-Yeon Wei

  • 14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with >0.1 timing error rate tolerance for IoT applications

    Paul N. Whatmough;Sae Kyu Lee;Hyunkwang Lee;Saketh Rama

  • Ares: a framework for quantifying the resilience of deep neural networks

    Brandon Reagen;Udit Gupta;Lillian Pentecost;Paul Whatmough

  • Adaptive bandwidth DLLs and PLLs using regulated supply CMOS buffers

    S. Sidiropoulos;Dean Liu;Jaeha Kim;Guyeon Wei

  • A review of actuation and power electronics options for flapping-wing robotic insects

    M. Karpelson;Gu-Yeon Wei;R.J. Wood

  • A variable-frequency parallel I/O interface with adaptive power supply regulation

    Gu-Yeon Wei;Jaeha Kim;D. Liu;S. Sidiropoulos

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • A low power switching power supply for self-clocked systems

    Gu-Yeon Wei;M. Horowitz

  • An adaptive PAM-4 5-Gb/s backplane transceiver in 0.25-/spl mu/m CMOS

    J.T. Stonick;Gu-Yeon Wei;J.L. Sonntag;D.K. Weinlader

  • Survey of Hardware Systems for Wireless Sensor Networks

    Mark Hempstead;Michael J. Lyons;David M. Brooks;Gu-Yeon Wei

Frequent Co-Authors

David Brooks
David Brooks Harvard University
Pavan Kumar Hanumolu
Pavan Kumar Hanumolu University of Illinois at Urbana-Champaign
Un-Ku Moon
Un-Ku Moon Oregon State University
Mark Horowitz
Mark Horowitz Stanford University
Pradip Bose
Pradip Bose IBM (United States)
Rob A. Rutenbar
Rob A. Rutenbar University of Pittsburgh
Aleksandar Kavcic
Aleksandar Kavcic University of Hawaii at Manoa
Daniel J. Friedman
Daniel J. Friedman IBM (United States)
Mircea R. Stan
Mircea R. Stan University of Virginia
Bryan K. Casper
Bryan K. Casper Intel (United States)

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