World's Best Scientists 2026 revealed!
Zhongfeng Wang

Zhongfeng Wang

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
35
Citations
4952
World Ranking
5584
National Ranking
804

Overview

Zhongfeng Wang is affiliated with Nanjing University in China and has a significant body of research primarily in the fields of Computer Science and Engineering. Their work encompasses a broad range of subfields including Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, and Hardware and Architecture.

The scientist's main topics of research focus on advanced neural network applications, error correcting code techniques, advanced wireless communication techniques, advanced memory and neural computing, CCD and CMOS imaging sensors, image processing techniques and applications, and advanced vision and imaging.

Wang's recent published papers illustrate a strong engagement with emerging technologies and hardware-oriented innovations in neural computing and integrated systems. Notable papers include:

  • Wireless Multiferroic Memristor with Coupled Giant Impedance and Artificial Synapse Application, 2022, Advanced Electronic Materials
  • Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning, 2020, IEEE Transactions on Circuits & Systems II Express Briefs
  • A Flexible and Efficient FPGA Accelerator for Various Large-Scale and Lightweight CNNs, 2021, IEEE Transactions on Circuits and Systems I Regular Papers
  • An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers, 2022, IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Evaluations on Deep Neural Networks Training Using Posit Number System, 2020, IEEE Transactions on Computers

Frequent coauthors in Wang's publications include Jun Lin, Wendong Mao, Suwen Song, Meiqi Wang, and Jing Tian. These collaborations highlight ongoing research partnerships and joint contributions in related technical areas.

The scientist has contributed extensively to several reputable publication venues, with notable numbers of publications in the following:

  • arXiv (Cornell University)
  • IEEE Transactions on Circuits and Systems I Regular Papers
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • IEEE Transactions on Circuits & Systems II Express Briefs
  • IEEE Communications Letters

Wang's research spans over 308 publications in Computer Science and 168 in Engineering, reflecting consistent academic productivity over diverse yet interconnected areas of study. The focus on hardware-accelerated neural networks, wireless communication techniques, and image processing underlines expertise in both theoretical and applied domains.

Best Publications

  • Low-Complexity High-Speed Decoder Design for Quasi-Cyclic LDPC Codes

    Zhongfeng Wang;Zhiqiang Cui

  • High-throughput layered decoder implementation for quasi-cyclic LDPC codes

    Kai Zhang;Xinming Huang;Zhongfeng Wang

  • A High-Speed and Low-Complexity Architecture for Softmax Function in Deep Learning

    Meiqi Wang;Siyuan Lu;Danyang Zhu;Jun Lin

  • On finite precision implementation of low density parity check codes decoder

    T. Zhang;Z. Wang;K.K. Parhi

  • VLSI implementation issues of TURBO decoder design for wireless applications

    Zhongfeng Wang;H. Suzuki;K.K. Parhi

  • An Efficient VLSI Architecture for Nonbinary LDPC Decoders

    Jun Lin;Jin Sha;Zhongfeng Wang;Li Li

  • Efficient Hardware Architectures for Deep Convolutional Neural Network

    Jichen Wang;Jun Lin;Zhongfeng Wang

  • Error correction for multi-level NAND flash memory using Reed-Solomon codes

    Bainan Chen;Xinmiao Zhang;Zhongfeng Wang

  • Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer

    Siyuan Lu;Meiqi Wang;Shuang Liang;Jun Lin

  • High-Throughput Layered LDPC Decoding Architecture

    Zhiqiang Cui;Zhongfeng Wang;Youjian Liu

  • Design of Sequential Elements for Low Power Clocking System

    Peiyi Zhao;J McNeely;Weidong Kuang;Nan Wang

  • Area-efficient high-speed decoding schemes for turbo decoders

    Zhongfeng Wang;Zhipei Chi;K.K. Parhi

  • Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning

    Danyang Zhu;Siyuan Lu;Meiqi Wang;Jun Lin

  • Accelerating Recurrent Neural Networks: A Memory-Efficient Approach

    Zhisheng Wang;Jun Lin;Zhongfeng Wang

  • An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks

    Yizhi Wang;Jun Lin;Zhongfeng Wang

  • Efficient Decoder Design for Nonbinary Quasicyclic LDPC Codes

    Jun Lin;Jin Sha;Zhongfeng Wang;Li Li

  • Improved k-best sphere decoding algorithms for MIMO systems

    Qingwei Li;Zhongfeng Wang

  • High-Speed Low-Power Viterbi Decoder Design for TCM Decoders

    Jinjin He;Huaping Liu;Zhongfeng Wang;Xinming Huang

  • Flexible LDPC Decoder Design for Multigigabit-per-Second Applications

    Chuan Zhang;Zhongfeng Wang;Jin Sha;Li Li

  • A Memory Efficient Partially Parallel Decoder Architecture for Quasi-Cyclic LDPC Codes

    Zhongfeng Wang;Zhiqiang Cui

  • Multi-Gb/s LDPC Code Design and Implementation

    Jin Sha;Zhongfeng Wang;Minglun Gao;Li Li

  • E-LSTM: An Efficient Hardware Architecture for Long Short-Term Memory

    Meiqi Wang;Zhisheng Wang;Jinming Lu;Jun Lin

  • High performance, high throughput turbo/SOVA decoder design

    Zhongfeng Wang;K.K. Parhi

  • Evaluations on Deep Neural Networks Training Using Posit Number System

    Jinming Lu;Chao Fang;Mingyang Xu;Jun Lin

  • An Efficient and Flexible Accelerator Design for Sparse Convolutional Neural Networks

    Xiaoru Xie;Jun Lin;Zhongfeng Wang;Jinghe Wei

  • TIE: energy-efficient tensor train-based inference engine for deep neural network

    Chunhua Deng;Fangxuan Sun;Xuehai Qian;Jun Lin

  • Low Complexity Message Passing Detection Algorithm for Large-Scale MIMO Systems

    Jing Zeng;Jun Lin;Zhongfeng Wang

  • Fully-Parallel Area-Efficient Deep Neural Network Design Using Stochastic Computing

    Yi Xie;Siyu Liao;Bo Yuan;Yanzhi Wang

Frequent Co-Authors

Keshab K. Parhi
Keshab K. Parhi University of Minnesota
Xiaohu You
Xiaohu You Southeast University
Yanzhi Wang
Yanzhi Wang Northeastern University
Brian M. Sadler
Brian M. Sadler United States Army Research Laboratory
Jose Silva-Martinez
Jose Silva-Martinez Texas A&M University
Jaijeet Roychowdhury
Jaijeet Roychowdhury University of California, Berkeley
Tong Zhang
Tong Zhang Rensselaer Polytechnic Institute

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