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D-Index & Metrics

Electronics and Electrical Engineering

D-Index
37
Citations
6142
World Ranking
5101
National Ranking
1773

Overview

Michael Niemier is affiliated with the University of Notre Dame in the United States, working primarily in the fields of Engineering and Computer Science. Their research spans multiple subfields, including Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, and Computer Vision and Pattern Recognition.

The scientist's main research topics cover a range of areas related to memory and computing technologies. Key topics include Ferroelectric and Negative Capacitance Devices, Advanced Memory and Neural Computing, Semiconductor Materials and Devices, Advanced Data Storage Technologies, Network Packet Processing and Optimization, Machine Learning and Extreme Learning Machines, and Cryptography and Data Security.

Some of their recent publications are:

  • Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search, 2022, Nature Communications
  • In-Memory Computing with Associative Memories: A Cross-Layer Perspective, 2021, 2021 IEEE International Electron Devices Meeting (IEDM)
  • Computing-in-Memory for Performance and Energy-Efficient Homomorphic Encryption, 2020, IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • FeFET Multi-Bit Content-Addressable Memories for In-Memory Nearest Neighbor Search, 2021, IEEE Transactions on Computers
  • Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing, 2022, Scientific Reports

Frequent co-authors in the scientist's work include Xiaobo Sharon Hu, Ann Franchesca Laguna, Xunzhao Yin, Dayane Reis, and Arman Kazemi.

Publication venues where Michael Niemier has contributed multiple works include arXiv (Cornell University), IEEE Transactions on Electron Devices, IEEE Transactions on Computers, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, and IEEE Design and Test.

Best Publications

  • Scaling for edge inference of deep neural networks

    Xiaowei Xu;Yukun Ding;Sharon Xiaobo Hu;Michael Niemier

  • Ferroelectric ternary content-addressable memory for one-shot learning

    Kai Ni;Xunzhao Yin;Ann Franchesca Laguna;Siddharth Joshi

  • Problems in designing with QCAs: Layout = Timing

    Michael T. Niemier;Peter M. Kogge

  • Nanomagnet logic: progress toward system-level integration.

    M T Niemier;G H Bernstein;G Csaba;A Dingler

  • FeCAM: A Universal Compact Digital and Analog Content Addressable Memory Using Ferroelectric

    Xunzhao Yin;Chao Li;Qingrong Huang;Li Zhang

  • On-Chip Clocking for Nanomagnet Logic Devices

    Mohmmad Tanvir Alam;Mohammad Jafar Siddiq;Gary H Bernstein;Michael Niemier

  • Logic in wire: using quantum dots to implement a microprocessor

    M.T. Niemier;P.M. Kogge

  • Clocking structures and power analysis for nanomagnet-based logic devices

    M. Niemier;M. Alam;X. S. Hu;G. Bernstein

  • A ferroelectric field effect transistor based synaptic weight cell

    Matthew Jerry;Sourav Dutta;Arman Kazemi;Kai Ni

  • Analog Circuit Design Using Tunnel-FETs

    Behnam Sedighi;Xiaobo Sharon Hu;Huichu Liu;Joseph J. Nahas

  • An Ultra-Dense 2FeFET TCAM Design Based on a Multi-Domain FeFET Model

    Xunzhao Yin;Kai Ni;Dayane Reis;Suman Datta

  • Shape Engineering for Controlled Switching With Nanomagnet Logic

    M. T. Niemier;E. Varga;G. H. Bernstein;W. Porod

  • Ferroelectric FETs-Based Nonvolatile Logic-in-Memory Circuits

    Xunzhao Yin;Xiaoming Chen;Michael Niemier;Xiaobo Sharon Hu

  • A design of and design tools for a novel quantum dot based microprocessor

    Michael T. Niemier;Michael J. Kontz;Peter M. Kogge

  • Logic in wire: using quantum dots to implement a microprocessor

    M.T. Niemier;P.M. Kogge

  • Emerging Technology-Based Design of Primitives for Hardware Security

    Yu Bi;Kaveh Shamsi;Jiann-Shiun Yuan;Pierre-Emmanuel Gaillardon

  • Quantum-dot cellular automata (QCA) circuit partitioning: problem modeling and solutions

    D.A. Antonelli;D.Z. Chen;T.J. Dysart;X.S. Hu

  • Computing in memory with FeFETs

    Dayane Reis;Michael Niemier;X. Sharon Hu

  • Experimental Demonstration of Fanout for Nanomagnetic Logic

    E Varga;A Orlov;M T Niemier;X S Hu

  • Exploring and exploiting wire-level pipelining in emerging technologies

    Michael Thaddeus Niemier;Peter M. Kogge

  • Design and benchmarking of ferroelectric FET based TCAM

    Xunzhao Yin;Michael Niemier;X. Sharon Hu

  • Author Correction: Ferroelectric ternary content-addressable memory for one-shot learning

    Kai Ni;Xunzhao Yin;Ann Franchesca Laguna;Siddharth Joshi

Frequent Co-Authors

Xiaobo Sharon Hu
Xiaobo Sharon Hu University of Notre Dame
Wolfgang Porod
Wolfgang Porod University of Notre Dame
Gary H. Bernstein
Gary H. Bernstein University of Notre Dame
Suman Datta
Suman Datta Georgia Institute of Technology
Alexei O. Orlov
Alexei O. Orlov University of Notre Dame
Yier Jin
Yier Jin University of Florida
Paolo Lugli
Paolo Lugli Free University of Bozen-Bolzano
Pierre-Emmanuel Gaillardon
Pierre-Emmanuel Gaillardon University of Utah
Sung Kyu Lim
Sung Kyu Lim Georgia Institute of Technology
Shimeng Yu
Shimeng Yu Georgia Institute of Technology

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