88 citations · 119 across the 13 of their papers we have counts for
6 papers · 1 filter
Exploring Temporal Information Dynamics in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics…
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +3
Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks, which is suitable to be implemented on low-power mobile/edge devices. As…
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning
Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra +1
Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algo…
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +2
Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an e…
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Youngeun Kim, Hyoungseob Park, Abhishek Moitra +3
Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary s…
Neural Architecture Search for Spiking Neural Networks
Youngeun Kim, Yuhang Li, Hyoungseob Park +2
Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-s…