Neuromorphic Hardware

Neuromorphic Hardware is specialized computing hardware that embodies the structural and operational principles of biological neural systems, particularly through event-driven, low-power processing and parallel architectures designed to efficiently implement spiking neural networks at the edge.

6.3K

Publications

417.3K

Citations

17.4K

Authors

2.4K

Institutions

Publications per year

2017–2026

4.3K

Authors

17.4K

  1. GI
    Giacomo Indiveri

    ETH Zurich, Switzerland

    139 publications · 13.1K citations

    47

    h-index

  2. SY
    Shimeng Yu

    Georgia Institute of Technology, United States

    73 publications · 11.7K citations

    46

    h-index

  3. KR
    Kaushik Roy

    Purdue University West Lafayette, United States

    66 publications · 3.8K citations

    36

    h-index

  4. HW
    Huaqiang Wu

    Tsinghua University, China

    45 publications · 7.6K citations

    34

    h-index

  5. BG
    Bin Gao

    Tsinghua University, China

    47 publications · 6.5K citations

    34

    h-index

1–5 of 17.4K

Venues

  1. Nature Communications

    163 publications · 24.4K citations

    87

    h-index

  2. Advanced Functional Materials

    167 publications · 16.1K citations

    73

    h-index

  3. Advanced Materials

    122 publications · 15.6K citations

    69

    h-index

  4. Frontiers in Neuroscience

    185 publications · 14.5K citations

    58

    h-index

  5. ACS Applied Materials & Interfaces

    132 publications · 7.8K citations

    53

    h-index

1–5