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Yeshwanth Venkatesha
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2020 – today
- 2024
- [c10]Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda:
HaLo-FL: Hardware-Aware Low-Precision Federated Learning. DATE 2024: 1-6 - 2023
- [j2]Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda:
Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems. Neural Networks 168: 569-579 (2023) - [c9]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer, Priyadarshini Panda:
Exploring Temporal Information Dynamics in Spiking Neural Networks. AAAI 2023: 8308-8316 - [c8]Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda:
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing. ACM Great Lakes Symposium on VLSI 2023: 619-624 - [i11]Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park, Priyadarshini Panda:
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems. CoRR abs/2305.07135 (2023) - [i10]Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda:
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing. CoRR abs/2305.18416 (2023) - 2022
- [c7]Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda:
PrivateSNN: Privacy-Preserving Spiking Neural Networks. AAAI 2022: 1192-1200 - [c6]Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra, Priyadarshini Panda:
MIME: adapting a single neural network for multi-task inference with memory-efficient dynamic pruning. DAC 2022: 499-504 - [c5]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Priyadarshini Panda:
Neural Architecture Search for Spiking Neural Networks. ECCV (24) 2022: 36-56 - [c4]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, Priyadarshini Panda:
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks. ECCV (12) 2022: 102-120 - [c3]Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Priyadarshini Panda:
Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? ICASSP 2022: 71-75 - [i9]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Priyadarshini Panda:
Neural Architecture Search for Spiking Neural Networks. CoRR abs/2201.10355 (2022) - [i8]Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Priyadarshini Panda:
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks? CoRR abs/2202.03133 (2022) - [i7]Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park, Yuhang Li, Priyadarshini Panda:
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization. CoRR abs/2203.13321 (2022) - [i6]Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra, Priyadarshini Panda:
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning. CoRR abs/2204.05274 (2022) - [i5]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, Priyadarshini Panda:
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks. CoRR abs/2207.01382 (2022) - [i4]Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer, Priyadarshini Panda:
Exploring Temporal Information Dynamics in Spiking Neural Networks. CoRR abs/2211.14406 (2022) - 2021
- [j1]Yeshwanth Venkatesha, Youngeun Kim, Leandros Tassiulas, Priyadarshini Panda:
Federated Learning With Spiking Neural Networks. IEEE Trans. Signal Process. 69: 6183-6194 (2021) - [c2]Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda:
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks. DATE 2021: 1360-1365 - [i3]Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda:
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks. CoRR abs/2101.04354 (2021) - [i2]Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda:
PrivateSNN: Fully Privacy-Preserving Spiking Neural Networks. CoRR abs/2104.03414 (2021) - [i1]Yeshwanth Venkatesha, Youngeun Kim, Leandros Tassiulas, Priyadarshini Panda:
Federated Learning with Spiking Neural Networks. CoRR abs/2106.06579 (2021) - 2020
- [c1]Timothy Foldy-Porto, Yeshwanth Venkatesha, Priyadarshini Panda:
Activation Density Driven Efficient Pruning in Training. ICPR 2020: 8929-8936
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