Youngeun Kim

Youngeun Kim

PhD Student, Electrical Engineering, Yale University

Talks and presentations

Searching for Feedback Connection Architectures using Neural Architecture Search in Spiking Neural Networks

August 18, 2022

Talk, Center for Brain-Inspired Computing (C-BRIC), SRC,

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-sparsity activation. However, most prior SNN methods use forward-only ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence processing of binary information in SNNs. To address this, we introduce a novel Neural Architecture Search (NAS) approach for finding better SNN architectures.

Towards Deep, Interpretable, and Robust Spiking Neural Networks: Algorithmic Approaches

February 25, 2021

Talk, Center for Brain-Inspired Computing (C-BRIC), SRC,

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning due to their huge energy efficiency benefits on neuromorphic hardware. In this presentation, we suggest important techniques for training SNNs which bring a huge benefit in terms of latency, accuracy, interpretability, and robustness.