Showing 1 - 5 results of 5 for search 'Jangsaeng Kim', query time: 0.02s Refine Results
  1. 1
    Direct Gradient Calculation: Simple and Variation‐Tolerant On‐Chip Training Method for Neural Networks

    Direct Gradient Calculation: Simple and Variation‐Tolerant On‐Chip Training Method for Neural Networks by Hyungyo Kim, Joon Hwang, Dongseok Kwon, Jangsaeng Kim, Min-Kyu Park, Jiseong Im, Byung-Gook Park, Jong-Ho Lee

    Published 2021-08-01
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  2. 2
    Unveiled Influence of Sub‐gap Density of States on Low‐Frequency Noise in Si‐Doped ZnSnO TFTs: Does Correlated Mobility Fluctuation Model Suffice?

    Unveiled Influence of Sub‐gap Density of States on Low‐Frequency Noise in Si‐Doped ZnSnO TFTs: Does Correlated Mobility Fluctuation Model Suffice? by Wonjun Shin, Ji Ye Lee, Ryun‐Han Koo, Jangsaeng Kim, Jong‐Ho Lee, Sang Yeol Lee, Sung‐Tae Lee

    Published 2024-02-01
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  3. 3
    Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment

    Hardware-Based Spiking Neural Network Using a TFT-Type AND Flash Memory Array Architecture Based on Direct Feedback Alignment by Won-Mook Kang, Dongseok Kwon, Sung Yun Woo, Soochang Lee, Honam Yoo, Jangsaeng Kim, Byung-Gook Park, Jong-Ho Lee

    Published 2021-01-01
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  4. 4
    On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

    On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding by Jiseong Im, Jaehyeon Kim, Ho-Nam Yoo, Jong-Won Baek, Dongseok Kwon, Seongbin Oh, Jangsaeng Kim, Joon Hwang, Byung-Gook Park, Jong-Ho Lee

    Published 2022-01-01
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  5. 5
    On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices

    On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices by Dongseok Kwon, Suhwan Lim, Jong-Ho Bae, Sung-Tae Lee, Hyeongsu Kim, Young-Tak Seo, Seongbin Oh, Jangsaeng Kim, Kyuho Yeom, Byung-Gook Park, Jong-Ho Lee

    Published 2020-07-01
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