World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
4997
World Ranking
13532
National Ranking
5404

Overview

Andreas Gerstlauer is affiliated with The University of Texas at Austin in the United States. Their research contributions span several areas within computer science and engineering, with a particular emphasis on hardware design, parallel computing, and security.

Their recent publications include:

  • Exploiting Errors for Efficiency, 2020, ACM Computing Surveys
  • DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices, 2021, International Journal of Parallel Programming
  • Machine Learning-Based Microarchitecture-Level Power Modeling of CPUs, 2022, IEEE Transactions on Computers
  • Horizontal Side-Channel Vulnerabilities of Post-Quantum Key Exchange and Encapsulation Protocols, 2021, ACM Transactions on Embedded Computing Systems
  • Aging Compensation With Dynamic Computation Approximation, 2020, IEEE Transactions on Circuits and Systems I Regular Papers

Among frequent co-authors collaborating with Gerstlauer are Hussam Amrouch, Michael Orshansky, Jörg Henkel, Erika S. Alcorta, and Pranav Rama.

The scientist regularly publishes in several prominent venues including:

  • arXiv (Cornell University)
  • ACM Transactions on Embedded Computing Systems
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • IEEE Transactions on Circuits and Systems I Regular Papers
  • ACM Transactions on Design Automation of Electronic Systems

Gerstlauer's main fields of study encompass computer science and engineering. Their subfields of focus include:

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

The primary topics addressed in their work are:

  • Parallel Computing and Optimization Techniques
  • Low-power high-performance VLSI design
  • Advanced Memory and Neural Computing
  • Advanced Data Storage Technologies
  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning
  • Cryptographic Implementations and Security

Best Publications

  • DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters

    Zhuoran Zhao;Kamyar Mirzazad Barijough;Andreas Gerstlauer

  • Embedded System Design: Modeling, Synthesis and Verification

    Daniel D. Gajski;Samar Abdi;Andreas Gerstlauer;Gunar Schirner

  • Electronic System-Level Synthesis Methodologies

    A. Gerstlauer;C. Haubelt;A.D. Pimentel;T.P. Stefanov

  • RTOS Modeling for System Level Design

    Andreas Gerstlauer;Haobo Yu;Daniel D. Gajski

  • System Design: A Practical Guide with SpecC

    Daniel D. Gajski;Rainer Domer;Junyu Peng;Andreas Gerstlauer

  • Modeling and synthesis of quality-energy optimal approximate adders

    Jin Miao;Ku He;Andreas Gerstlauer;Michael Orshansky

  • System-on-chip environment: a SpecC-based framework for heterogeneous MPSoC design

    Rainer Dömer;Andreas Gerstlauer;Junyu Peng;Dongwan Shin

  • Reliability-aware design to suppress aging

    Hussam Amrouch;Behnam Khaleghi;Andreas Gerstlauer;Jorg Henkel

  • Approximate logic synthesis under general error magnitude and frequency constraints

    Jin Miao;Andreas Gerstlauer;Michael Orshansky

  • Codesign Tradeoffs for High-Performance, Low-Power Linear Algebra Architectures

    A. Pedram;R. A. van de Geijn;A. Gerstlauer

  • Accurate phase-level cross-platform power and performance estimation

    Xinnian Zheng;Lizy K. John;Andreas Gerstlauer

  • Retargetable profiling for rapid, early system-level design space exploration

    Lukai Cai;Andreas Gerstlauer;Daniel Gajski

  • High-level synthesis of approximate hardware under joint precision and voltage scaling

    Seogoo Lee;Lizy K. John;Andreas Gerstlauer

  • System-level abstraction semantics

    Andreas Gerstlauer;Daniel D. Gajski

  • RTOS scheduling in transaction level models

    Haobo Yu;Andreas Gerstlauer;Daniel Gajski

  • Multi-level approximate logic synthesis under general error constraints

    Jin Miao;Andreas Gerstlauer;Michael Orshansky

  • C-based Interactive RTL Design Methodology

    Dongwan Shin;Andreas Gerstlauer;Rainer Dömer;Daniel Gajski

  • The next generation of virtual prototyping: ultra-fast yet accurate simulation of HW/SW systems

    Oliver Bringmann;Wolfgang Ecker;Andreas Gerstlauer;Ajay Goyal

  • DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices

    Rafael Stahl;Alexander Hoffman;Daniel Mueller-Gritschneder;Andreas Gerstlauer

  • Abstract, Multifaceted Modeling of Embedded Processors for System Level Design

    G. Schirner;A. Gerstlauer;R. Domer

  • Exploiting Errors for Efficiency: A Survey from Circuits to Applications

    Phillip Stanley-Marbell;Armin Alaghi;Michael Carbin;Eva Darulova

  • Horizontal side-channel vulnerabilities of post-quantum key exchange protocols

    Aydin Aysu;Youssef Tobah;Mohit Tiwari;Andreas Gerstlauer

Frequent Co-Authors

Daniel D. Gajski
Daniel D. Gajski University of California, Irvine
Lizy K. John
Lizy K. John The University of Texas at Austin
Jorg Henkel
Jorg Henkel Karlsruhe Institute of Technology
Michael Orshansky
Michael Orshansky The University of Texas at Austin
Robert A. van de Geijn
Robert A. van de Geijn The University of Texas at Austin
Robert W. Heath
Robert W. Heath University of California, San Diego
Sriram Vishwanath
Sriram Vishwanath The University of Texas at Austin
Jürgen Teich
Jürgen Teich University of Erlangen-Nuremberg
Ulf Schlichtmann
Ulf Schlichtmann Technical University of Munich
George Biros
George Biros The University of Texas at Austin

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a variety of careers, and students often explore closely related disciplines through online degree programs. Popular pathways include environmental engineering, mechanical engineering, physics, and data science, each offering unique opportunities in tech-driven industries.

For those interested in sustainability and technology, consider an online environmental engineering degree science and engineering program. This interdisciplinary path focuses on solving real-world environmental problems using modern computing tools.

Engineering remains a top choice for tech-focused students. With options like a mechanical engineering degree online cost-effective programs, learners can gain technical skills crucial for robotics, automation, and innovation.

Physics is foundational for computational thinking and analytical roles. If affordability is a concern, explore a cheapest online physics degree to build core scientific knowledge while keeping costs manageable.

Finally, the data revolution has made data science one of the most sought-after fields. Earning a data scientist degree online offers access to careers in analytics, AI, and business intelligence—all aligned with computer science expertise.

Best Scientists Citing Andreas Gerstlauer

Trending Scientists

Recently Published Articles