World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
15169
World Ranking
4234
National Ranking
1998

Electronics and Electrical Engineering

D-Index
54
Citations
14758
World Ranking
2246
National Ranking
878

Research.com Recognitions

  • 2020 - ACM Fellow For contribution to design and modeling of power-efficient computer architectures
  • 2016 - IEEE Fellow For contribution to circuits and architectures for power-efficient microprocessors

Overview

Nam Sung Kim is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research spans fields including computer science and engineering, with a particular focus on areas such as computer networks and communications, electrical and electronic engineering, hardware and architecture, information systems, and artificial intelligence.

The scientist's work addresses several key topics: parallel computing and optimization techniques, advanced data storage technologies, semiconductor materials and devices, cloud computing and resource management, advanced memory and neural computing, ferroelectric and negative capacitance devices, and caching and content delivery.

Frequent co-authors collaborating with Nam Sung Kim are Chihun Song, Ipoom Jeong, Houxiang Ji, Youjie Li, and Jung Ho Ahn.

Nam Sung Kim has published a significant number of articles in various academic venues, with notable frequent publication venues including:

  • arXiv (Cornell University)
  • IEEE Computer Architecture Letters
  • IEEE Micro
  • IEEE Transactions on Computers
  • IEEE Journal of Solid-State Circuits

Selected recent papers by Nam Sung Kim include:

  • Near-Memory Processing in Action: Accelerating Personalized Recommendation With AxDIMM, 2021, IEEE Micro
  • Aquabolt-XL HBM2-PIM, LPDDR5-PIM With In-Memory Processing, and AXDIMM With Acceleration Buffer, 2022, IEEE Micro
  • A 16-GB 640-GB/s HBM2E DRAM With a Data-Bus Window Extension Technique and a Synergetic On-Die ECC Scheme, 2020, IEEE Journal of Solid-State Circuits
  • DML: Dynamic Partial Reconfiguration With Scalable Task Scheduling for Multi-Applications on FPGAs, 2021, IEEE Transactions on Computers
  • BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling, 2022, arXiv (Cornell University)

Nam Sung Kim has received recognition for contributions to their field, including being named an ACM Fellow in 2020 for work on the design and modeling of power-efficient computer architectures. In 2016, they were also elected IEEE Fellow for contributions to circuits and architectures for power-efficient microprocessors.

Best Publications

  • Razor: a low-power pipeline based on circuit-level timing speculation

    Dan Ernst;Nam Sung Kim;Shidhartha Das;Sanjay Pant

  • Leakage current: Moore's law meets static power

    N.S. Kim;T. Austin;D. Baauw;T. Mudge

  • Drowsy caches: simple techniques for reducing leakage power

    Krisztián Flautner;Nam Sung Kim;Steve Martin;David Blaauw

  • GPUWattch: enabling energy optimizations in GPGPUs

    Jingwen Leng;Tayler Hetherington;Ahmed ElTantawy;Syed Gilani

  • Approximate Computing: A Survey

    Qiang Xu;Todd Mytkowicz;Nam Sung Kim

  • Razor: circuit-level correction of timing errors for low-power operation

    D. Ernst;S. Das;S. Lee;D. Blaauw

  • Energy-Efficient and Metastability-Immune Resilient Circuits for Dynamic Variation Tolerance

    Keith A. Bowman;James W. Tschanz;Nam Sung Kim;Janice C. Lee

  • NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules

    Amin Farmahini-Farahani;Jung Ho Ahn;Katherine Morrow;Nam Sung Kim

  • Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications

    Srinivasan Narayanamoorthy;Hadi Asghari Moghaddam;Zhenhong Liu;Taejoon Park

  • Adaptive Frequency and Biasing Techniques for Tolerance to Dynamic Temperature-Voltage Variations and Aging

    J. Tschanz;Nam Sung Kim;S. Dighe;J. Howard

  • Circuit and microarchitectural techniques for reducing cache leakage power

    Nam Sung Kim;K. Flautner;D. Blaauw;T. Mudge

  • Drowsy instruction caches. Leakage power reduction using dynamic voltage scaling and cache sub-bank prediction

    Nam Sung Kim;Krisztián Flautner;David Blaauw;Trevor Mudge

  • The case for GPGPU spatial multitasking

    Jacob T. Adriaens;Katherine Compton;Nam Sung Kim;Michael J. Schulte

  • Yield-driven near-threshold SRAM design

    Gregory K. Chen;David Blaauw;Trevor Mudge;Dennis Sylvester

  • Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology : Industrial Product

    Sukhan Lee;Shin-haeng Kang;Jaehoon Lee;Hyeonsu Kim

  • Power-efficient computing for compute-intensive GPGPU applications

    S. Z. Gilani;Nam Sung Kim;M. J. Schulte

  • 25.4 A 20nm 6GB Function-In-Memory DRAM, Based on HBM2 with a 1.2TFLOPS Programmable Computing Unit Using Bank-Level Parallelism, for Machine Learning Applications

    Young-Cheon Kwon;Suk Han Lee;Jaehoon Lee;Sang-Hyuk Kwon

  • Wordline & Bitline Pulsing Schemes for Improving SRAM Cell Stability in Low-Vcc 65nm CMOS Designs

    M. Khellah;Y. Ye;N. Kim;D. Somasekhar

  • VARIUS-NTV: A microarchitectural model to capture the increased sensitivity of manycores to process variations at near-threshold voltages

    Ulya R. Karpuzcu;Krishna B. Kolluru;Nam Sung Kim;Josep Torrellas

  • GPU register file virtualization

    Hyeran Jeon;Gokul Subramanian Ravi;Nam Sung Kim;Murali Annavaram

  • Optimizing throughput of power- and thermal-constrained multicore processors using DVFS and per-core power-gating

    Jungseob Lee;Nam Sung Kim

  • Chameleon: versatile and practical near-DRAM acceleration architecture for large memory systems

    Hadi Asghari-Moghaddam;Young Hoon Son;Jung Ho Ahn;Nam Sung Kim

Frequent Co-Authors

Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Vivek De
Vivek De Intel (United States)
Michael J. Schulte
Michael J. Schulte Advanced Micro Devices (United States)
Muhammad M. Khellah
Muhammad M. Khellah Intel (United States)
Dinesh Somasekhar
Dinesh Somasekhar Intel (United States)
Jung Ho Ahn
Jung Ho Ahn Seoul National University
Tanay Karnik
Tanay Karnik Intel (United States)
Mikko H. Lipasti
Mikko H. Lipasti University of Wisconsin–Madison
David Blaauw
David Blaauw University of Michigan–Ann Arbor
Hadi Esmaeilzadeh
Hadi Esmaeilzadeh University of California, San Diego

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