2026 Michael J. Schulte: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	47	6605	6414	2917	2809	223	7015

Research.com Recognitions

2017 - IEEE Fellow For contributions to compute architectures

Overview

Michael J. Schulte is affiliated with Advanced Micro Devices in the United States. Their research spans multiple topics within computer science, with a specific focus on hardware and architecture, computer networks and communications, artificial intelligence, materials chemistry, and molecular biology.

The scientist's work covers a range of main topics including:

Parallel Computing and Optimization Techniques
Distributed Systems and Fault Tolerance
Security and Verification in Computing
Machine Learning in Materials Science
Genetics, Bioinformatics, and Biomedical Research
Scientific Computing and Data Management
Numerical Methods and Algorithms

Michael J. Schulte has published papers in various venues, including:

IEEE Micro
arXiv (Cornell University)
TUGraz OPEN Library (Graz University of Technology)

Recent publications include:

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies, 2023, arXiv (Cornell University)
What Made Us Stronger: An Inside Look Back at the History of AMD Microprocessor Development, 2021, IEEE Micro
A High Radix On-line Arithmetic for Credible and Accurate Computing, 2020, TUGraz OPEN Library (Graz University of Technology)

Frequent coauthors of Michael J. Schulte are:

Dave Christie
Mike Clark
Shuaiwen Leon Song
Bonnie Kruft
Minjia Zhang

Michael J. Schulte was awarded the IEEE Fellow distinction in 2017 for contributions to compute architectures.

Best Publications

Truncated multiplication with correction constant [for DSP]

M.J. Schulte;E.E. Swartzlander

331
Citations
The case for GPGPU spatial multitasking

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

246
Citations
An overview of reconfigurable hardware in embedded systems

Philip Garcia;Katherine Compton;Michael Schulte;Emily Blem

226
Citations
Approximating elementary functions with symmetric bipartite tables

M.J. Schulte;J.E. Stine

226
Citations
Decimal multiplication via carry-save addition

M.A. Erle;M.J. Schulte

199
Citations
The Symmetric Table Addition Method for Accurate Function Approximation

James E. Stine;Michael J. Schulte

184
Citations
Power-efficient computing for compute-intensive GPGPU applications

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

181
Citations
Hardware designs for exactly rounded elementary functions

M.J. Schulte;E.E. Swartzlander

168
Citations
Decimal multiplication with efficient partial product generation

M.A. Erle;E.M. Schwarz;M.J. Schulte

160
Citations
High-speed multioperand decimal adders

R.D. Kenney;M.J. Schulte

147
Citations
Symmetric bipartite tables for accurate function approximation

M.J. Schulte;J.E. Stine

125
Citations
Reduced power dissipation through truncated multiplication

M.J. Schulte;J.E. Stine;J.G. Jansen

124
Citations
Analysis of column compression multipliers

K.C. Bickerstaff;E.E. Swartzlander;M.J. Schulte

113
Citations
Achieving Exascale Capabilities through Heterogeneous Computing

Michael J. Schulte;Mike Ignatowski;Gabriel H. Loh;Bradford M. Beckmann

111
Citations
Reduced area multipliers

K.A.C. Bickerstaff;M. Schulte;E.E. Swartzlander

107
Citations
Lossless and lossy memory I/O link compression for improving performance of GPGPU workloads

Vijay Sathish;Michael J. Schulte;Nam Sung Kim

107
Citations
Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage/Frequency and Core Scaling

Jungseob Lee;Vijay Sathisha;Michael Schulte;Katherine Compton

106
Citations
A high-frequency decimal multiplier

R.D. Kenney;M.J. Schulte;M.A. Erle

105
Citations
A family of variable-precision interval arithmetic processors

M.J. Schulte;E.E. Swartzlander

104
Citations
AUDIT: Stress Testing the Automatic Way

Youngtaek Kim;Lizy Kurian John;Sanjay Pant;Srilatha Manne

99
Citations
Die-stacked memory device providing data translation

Gabriel H. Loh;Bradford M. Beckmann;James M. O'Connor;Michael Ignatowski

97
Citations

Frequent Co-Authors

Nam Sung Kim University of Illinois at Urbana-Champaign

Stamatis Vassiliadis Delft University of Technology

Gabriel H. Loh Advanced Micro Devices (United States)

Earl E. Swartzlander The University of Texas at Austin

Lizy K. John The University of Texas at Austin

Yu Hen Hu University of Wisconsin–Madison

Steven K. Reinhardt Advanced Micro Devices (United States)

Walter F. Block University of Wisconsin–Madison

Shuvra S. Bhattacharyya University of Maryland, College Park

Parameswaran Ramanathan University of Wisconsin–Madison

External Links

Personal website of Michael J. Schulte Google Scholar page

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

Related Online Degrees & Career Pathways

Expanding your education in computer science opens up many options for career growth and specialization. If you want to quickly boost your credentials, consider easy licenses and certifications to get. These can lead to well-paying roles and often require less time and investment than a traditional degree.

For those aiming for rapid advancement, there are also shortest masters degree programs online that can help you earn a respected credential faster. These accelerated programs are ideal for working professionals or students eager to move their careers forward.

If you’re thinking about which advanced degrees offer the best return on investment, explore some of the masters degrees that are worth it in the tech field. These programs are designed to align with high-demand industries and current job market trends.

Alternatively, starting with online associate degree programs is a smart way to get foundational knowledge and enter the tech workforce sooner. Many employers recognize these degrees, making them a practical stepping stone to a rewarding IT career.