World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
10541
World Ranking
4365
National Ranking
2039

Research.com Recognitions

  • 2020 - Fellow, National Academy of Inventors
  • 2020 - ACM Fellow For contributions to the design, modeling and benchmarking of computer architectures
  • 2009 - IEEE Fellow For contributions to power modeling and performance evaluation of microprocessors

Overview

Lizy K. John is affiliated with The University of Texas at Austin in the United States. Their research primarily focuses on computer science, with specializations in hardware and architecture, electrical and electronic engineering, computer networks and communications, artificial intelligence, and computer vision and pattern recognition.

Their work encompasses a range of topics including:

  • Parallel Computing and Optimization Techniques
  • Advanced Memory and Neural Computing
  • Embedded Systems Design Techniques
  • Advanced Data Storage Technologies
  • Interconnection Networks and Systems
  • VLSI and FPGA Design Techniques
  • Low-power high-performance VLSI design

Frequent co-authors in their publications include Aman Arora, Zachary Susskind, Felipe M. G. França, Alan T. L. Bacellar, and Bagus Hanindhito.

Lizy K. John's research contributions have appeared in various publication venues, notably:

  • IEEE Micro
  • arXiv (Cornell University)
  • ACM Transactions on Architecture and Code Optimization
  • Zenodo (CERN European Organization for Nuclear Research)
  • ACM Transactions on Reconfigurable Technology and Systems

Selected recent papers by Lizy K. John include:

  • Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterization, 2021, arXiv (Cornell University)
  • Koios 2.0: Open-Source Deep Learning Benchmarks for FPGA Architecture and CAD Research, 2023, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Thermal-Aware Design Space Exploration of 3-D Systolic ML Accelerators, 2021, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
  • CoMeFa: Deploying Compute-in-Memory on FPGAs for Deep Learning Acceleration, 2023, ACM Transactions on Reconfigurable Technology and Systems
  • Tensor Slices: FPGA Building Blocks For The Deep Learning Era, 2022, ACM Transactions on Reconfigurable Technology and Systems

Their research career includes recognition as an ACM Fellow (2020) for contributions to the design, modeling, and benchmarking of computer architectures, and being named a Fellow of the National Academy of Inventors in 2020. Earlier, in 2009, they were honored as an IEEE Fellow for contributions to power modeling and performance evaluation of microprocessors.

Best Publications

  • Scaling to the end of silicon with EDGE architectures

    D. Burger;S.W. Keckler;K.S. McKinley;M. Dahlin

  • Digital Systems Design Using VHDL

    Charles H. Roth;Lizy K. John

  • Run-time modeling and estimation of operating system power consumption

    Tao Li;Lizy Kurian John

  • A novel low power energy recovery full adder cell

    R. Shalem;E. John;L.K. John

  • Using complete machine simulation for software power estimation: the SoftWatt approach

    S. Gurumurthi;A. Sivasubramaniam;M.J. Irwin;N. Vijaykrishnan

  • Complete System Power Estimation Using Processor Performance Events

    W. L. Bircher;L. K. John

  • Analysis of redundancy and application balance in the SPEC CPU2006 benchmark suite

    Aashish Phansalkar;Ajay Joshi;Lizy K. John

  • Complete System Power Estimation: A Trickle-Down Approach Based on Performance Events

    W.L. Bircher;L.K. John

  • Minimalist open-page: a DRAM page-mode scheduling policy for the many-core era

    Dimitris Kaseridis;Jeffrey Stuecheli;Lizy Kurian John

  • Measuring Program Similarity: Experiments with SPEC CPU Benchmark Suites

    Aashish Phansalkar;Ajay Joshi;L. Eeckhout;L.K. John

  • Performance prediction based on inherent program similarity

    Kenneth Hoste;Aashish Phansalkar;Lieven Eeckhout;Andy Georges

  • Measuring benchmark similarity using inherent program characteristics

    Ajay Joshi;Aashish Phansalkar;L. Eeckhout;L.K. John

  • Control Flow Modeling in Statistical Simulation for Accurate and Efficient Processor Design Studies

    Lieven Eeckhout;Robert H. Bell;Bastiaan Stougie;Koen De Bosschere

  • The virtual write queue: coordinating DRAM and last-level cache policies

    Jeffrey Stuecheli;Dimitris Kaseridis;David Daly;Hillery C. Hunter

  • Bottlenecks in multimedia processing with SIMD style extensions and architectural enhancements

    D. Talla;L.K. John;D. Burger

  • Runtime identification of microprocessor energy saving opportunities

    W. L. Bircher;M. Valluri;J. Law;L. K. John

  • Evaluating MMX technology using DSP and multimedia applications

    Ravi Bhargava;Lizy K. John;Brian L. Evans;Ramesh Radhakrishnan

  • Elastic Refresh: Techniques to Mitigate Refresh Penalties in High Density Memory

    Jeffrey Stuecheli;Dimitris Kaseridis;Hillery C.Hunter;Lizy K. John

  • Is Compiling for Performance — Compiling for Power?

    Madhavi Valluri;Lizy K. John

  • Efficient program scheduling for heterogeneous multi-core processors

    Jian Chen;Lizy K. John

  • Performance Evaluation and Benchmarking

    Lizy Kurian John;Lieven Eeckhout

Frequent Co-Authors

Andreas Gerstlauer
Andreas Gerstlauer The University of Texas at Austin
Lieven Eeckhout
Lieven Eeckhout Ghent University
Tao Li
Tao Li University of Florida
Anand Sivasubramaniam
Anand Sivasubramaniam Pennsylvania State University
David J. Lilja
David J. Lilja University of Minnesota
Hai Jin
Hai Jin Huazhong University of Science and Technology
N. Vijaykrishnan
N. Vijaykrishnan Pennsylvania State University
Michael J. Schulte
Michael J. Schulte Advanced Micro Devices (United States)
Koen De Bosschere
Koen De Bosschere Ghent University
Brian L. Evans
Brian L. Evans The University of Texas at Austin

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