World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
46
Citations
10427
World Ranking
3334
National Ranking
150

Overview

Jie Han is affiliated with the University of Alberta in Canada and has a research portfolio primarily rooted in computer science and engineering. Their work spans various subfields including electrical and electronic engineering, computational theory and mathematics, artificial intelligence, computer networks and communications, and molecular biology.

The core areas of Jie Han's research focus on:

  • Low-power high-performance VLSI design
  • Error correcting code techniques
  • Advancements in semiconductor devices and circuit design
  • Numerical methods and algorithms
  • Quantum computing algorithms and architecture
  • Limits and structures in graph theory
  • Advanced graph theory research

They have contributed extensively to scholarly literature, with notable recent papers including:

  • Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications, 2020, Proceedings of the IEEE
  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • An Improved Logarithmic Multiplier for Energy-Efficient Neural Computing, 2020, IEEE Transactions on Computers
  • High Performance CNN Accelerators Based on Hardware and Algorithm Co-Optimization, 2020, IEEE Transactions on Circuits and Systems I Regular Papers
  • Hybrid Partial Product-Based High-Performance Approximate Recursive Multipliers, 2020, IEEE Transactions on Emerging Topics in Computing

Jie Han frequently collaborates with several researchers, including Tingting Zhang, Honglan Jiang, Leibo Liu, Guangjun Xie, and Fabrizio Lombardi.

Their publications are commonly found in well-regarded venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Circuits and Systems I Regular Papers
  • IEEE Transactions on Nanotechnology
  • IEEE Transactions on Emerging Topics in Computing
  • IEEE Transactions on Circuits & Systems II Express Briefs

This range of publication venues reflects the interdisciplinary nature of Jie Han's research, spanning theoretical, applied, and emerging areas within computer science and engineering disciplines.

Best Publications

  • Approximate computing: An emerging paradigm for energy-efficient design

    Jie Han;Michael Orshansky

  • Design and Analysis of Approximate Compressors for Multiplication

    Amir Momeni;Jie Han;Paolo Montuschi;Fabrizio Lombardi

  • New Metrics for the Reliability of Approximate and Probabilistic Adders

    Jinghang Liang;Jie Han;F. Lombardi

  • Approximate XOR/XNOR-based adders for inexact computing

    Zhixi Yang;Ajaypat Jain;Jinghang Liang;Jie Han

  • A low-power, high-performance approximate multiplier with configurable partial error recovery

    Cong Liu;Jie Han;Fabrizio Lombardi

  • Design of Approximate Radix-4 Booth Multipliers for Error-Tolerant Computing

    Weiqiang Liu;Liangyu Qian;Chenghua Wang;Honglan Jiang

  • Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications

    Honglan Jiang;Francisco Javier Hernandez Santiago;Hai Mo;Leibo Liu

  • A Review, Classification, and Comparative Evaluation of Approximate Arithmetic Circuits

    Honglan Jiang;Cong Liu;Leibo Liu;Fabrizio Lombardi

  • A system architecture solution for unreliable nanoelectronic devices

    Jie Han;P. Jonker

  • Approximate Radix-8 Booth Multipliers for Low-Power and High-Performance Operation

    Honglan Jiang;Jie Han;Fei Qiao;Fabrizio Lombardi

  • Low-Power Approximate Multipliers Using Encoded Partial Products and Approximate Compressors

    Mohammad Saeed Ansari;Honglan Jiang;Bruce F. Cockburn;Jie Han

  • A Comparative Review and Evaluation of Approximate Adders

    Honglan Jiang;Jie Han;Fabrizio Lombardi

  • A Survey of Coarse-Grained Reconfigurable Architecture and Design: Taxonomy, Challenges, and Applications

    Leibo Liu;Jianfeng Zhu;Zhaoshi Li;Yanan Lu

  • Toward hardware-redundant, fault-tolerant logic for nanoelectronics

    Jie Han;J. Gao;P. Jonker;Yan Qi

  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications

    Yidong Liu;Siting Liu;Yanzhi Wang;Fabrizio Lombardi

  • Reliability evaluation of logic circuits using probabilistic gate models

    Jie Han;Hao Chen;Erin Boykin;José A. B. Fortes

  • A Stochastic Computational Approach for Accurate and Efficient Reliability Evaluation

    Jie Han;Hao Chen;Jinghang Liang;Peican Zhu

  • Design and Evaluation of Multiple Valued Logic Gates Using Pseudo N-Type Carbon Nanotube FETs

    Jinghang Liang;Linbin Chen;Jie Han;Fabrizio Lombardi

  • A defect-?and fault-tolerant architecture for nanocomputers

    Jie Han;Pieter Jonker

  • Approximate compressors for error-resilient multiplier design

    Zhixi Yang;Jie Han;Fabrizio Lombardi

  • A Stochastic Computational Multi-Layer Perceptron with Backward Propagation

    Yidong Liu;Siting Liu;Yanzhi Wang;Fabrizio Lombardi

Frequent Co-Authors

Fabrizio Lombardi
Fabrizio Lombardi Northeastern University
Shaojun Wei
Shaojun Wei Tsinghua University
Shouyi Yin
Shouyi Yin Tsinghua University
Yanzhi Wang
Yanzhi Wang Northeastern University
Weisheng Zhao
Weisheng Zhao Beihang University
Huazhong Yang
Huazhong Yang Tsinghua University
Deliang Fan
Deliang Fan Johns Hopkins University
Shaahin Angizi
Shaahin Angizi New Jersey Institute of Technology
Witold Pedrycz
Witold Pedrycz University of Alberta
Nobuyuki Yoshikawa
Nobuyuki Yoshikawa Yokohama National University

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