25 citations · 29 across the 5 of their papers we have counts for
5 papers
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…
AdvRush: Searching for Adversarially Robust Neural Architectures
Jisoo Mok, Byunggook Na, Hyeokjun Choe +1
Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptib…
Accelerating Neural Architecture Search via Proxy Data
Byunggook Na, Jisoo Mok, Hyeokjun Choe +1
Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost o…
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…
Near-Data Processing for Differentiable Machine Learning Models
Hyeokjun Choe, Seil Lee, Hyunha Nam +4
Near-data processing (NDP) refers to augmenting memory or storage with processing power. Despite its potential for acceleration computing and reducing power requirements, only limi…