25 citations · 31 across the 5 of their papers we have counts for
6 papers
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
Jongwan Kim, DongJin Lee, Byunggook Na +3
Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic…
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…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
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…