380 citations · 1.6k across the 84 of their papers we have counts for
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AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na, Jisoo Mok, Seongsik Park +3
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, there…
Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding
Seongsik Park, Sungroh Yoon
The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the hu…
Energy-efficient Knowledge Distillation for Spiking Neural Networks
Dongjin Lee, Seongsik Park, Jongwan Kim +2
Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation.…
Noise-Robust Deep Spiking Neural Networks with Temporal Information
Seongsik Park, Dongjin Lee, Sungroh Yoon
Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the de…
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Seongsik Park, Seijoon Kim, Byunggook Na +1
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their appli…
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
Seongsik Park, Seijoon Kim, Hyeokjun Choe +1
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion o…
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets
Jaekoo Lee, Hyunjae Kim, Jongsun Lee +1
Graphs provide a powerful means for representing complex interactions between entities. Recently, deep learning approaches are emerging for representing and modeling graph-structur…