Search Results - Venkatesha, Yeshwanth

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  1. 1
    Federated Learning With Spiking Neural Networks

    Federated Learning With Spiking Neural Networks by Venkatesha, Yeshwanth, Kim, Youngeun, Tassiulas, Leandros, Panda, Priyadarshini

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  2. 2
    Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems

    Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems by Venkatesha, Yeshwanth, Kim, Youngeun, Park, Hyoungseob, Panda, Priyadarshini

    Published in Neural networks
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  3. 3
    MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning

    MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning by Bhattacharjee, Abhiroop, Yeshwanth Venkatesha, Moitra, Abhishek, Panda, Priyadarshini

    Published in arXiv.org
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  4. 4
    Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing

    Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing by Bhattacharjee, Abhiroop, Moitra, Abhishek, Kim, Youngeun, Yeshwanth Venkatesha, Panda, Priyadarshini

    Published in arXiv.org
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  5. 5
    Federated Learning with Spiking Neural Networks

    Federated Learning with Spiking Neural Networks by Yeshwanth Venkatesha, Kim, Youngeun, Tassiulas, Leandros, Panda, Priyadarshini

    Published in arXiv.org
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  6. 6
    HaLo-FL: Hardware-Aware Low-Precision Federated Learning

    HaLo-FL: Hardware-Aware Low-Precision Federated Learning by Venkatesha, Yeshwanth, Bhattacharjee, Abhiroop, Moitra, Abhishek, Panda, Priyadarshini

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  7. 7
    Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?

    Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? by Kim, Youngeun, Park, Hyoungseob, Moitra, Abhishek, Bhattacharjee, Abhiroop, Venkatesha, Yeshwanth, Panda, Priyadarshini

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  8. 8
    Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks

    Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks by Vasquez, Karina, Venkatesha, Yeshwanth, Bhattacharjee, Abhiroop, Moitra, Abhishek, Panda, Priyadarshini

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  9. 9
    PrivateSNN: Privacy-Preserving Spiking Neural Networks

    PrivateSNN: Privacy-Preserving Spiking Neural Networks by Kim, Youngeun, Venkatesha, Yeshwanth, Panda, Priyadarshini

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  10. 10
    Exploring Temporal Information Dynamics in Spiking Neural Networks

    Exploring Temporal Information Dynamics in Spiking Neural Networks by Kim, Youngeun, Li, Yuhang, Park, Hyoungseob, Venkatesha, Yeshwanth, Hambitzer, Anna, Panda, Priyadarshini

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  11. 11
    PrivateSNN: Privacy-Preserving Spiking Neural Networks

    PrivateSNN: Privacy-Preserving Spiking Neural Networks by Kim, Youngeun, Yeshwanth Venkatesha, Panda, Priyadarshini

    Published in arXiv.org
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  12. 12
    Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems

    Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems by Yeshwanth Venkatesha, Kim, Youngeun, Park, Hyoungseob, Panda, Priyadarshini

    Published in arXiv.org
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  13. 13
    Neural Architecture Search for Spiking Neural Networks

    Neural Architecture Search for Spiking Neural Networks by Kim, Youngeun, Li, Yuhang, Park, Hyoungseob, Yeshwanth Venkatesha, Panda, Priyadarshini

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  14. 14
    Activation Density driven Energy-Efficient Pruning in Training

    Activation Density driven Energy-Efficient Pruning in Training by Foldy-Porto, Timothy, Yeshwanth Venkatesha, Panda, Priyadarshini

    Published in arXiv.org
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  15. 15
    Addressing Client Drift in Federated Continual Learning with Adaptive Optimization

    Addressing Client Drift in Federated Continual Learning with Adaptive Optimization by Yeshwanth Venkatesha, Kim, Youngeun, Park, Hyoungseob, Li, Yuhang, Panda, Priyadarshini

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  16. 16
    Exploring Temporal Information Dynamics in Spiking Neural Networks

    Exploring Temporal Information Dynamics in Spiking Neural Networks by Kim, Youngeun, Li, Yuhang, Park, Hyoungseob, Yeshwanth Venkatesha, Hambitzer, Anna, Panda, Priyadarshini

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  17. 17
    Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks

    Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks by Vasquez, Karina, Yeshwanth Venkatesha, Bhattacharjee, Abhiroop, Moitra, Abhishek, Panda, Priyadarshini

    Published in arXiv.org
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  18. 18
    Exploring Lottery Ticket Hypothesis in Spiking Neural Networks

    Exploring Lottery Ticket Hypothesis in Spiking Neural Networks by Kim, Youngeun, Li, Yuhang, Park, Hyoungseob, Yeshwanth Venkatesha, Yin, Ruokai, Panda, Priyadarshini

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  19. 19
    Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?

    Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks? by Kim, Youngeun, Park, Hyoungseob, Moitra, Abhishek, Bhattacharjee, Abhiroop, Yeshwanth Venkatesha, Panda, Priyadarshini

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