31 citations · 36 across the 4 of their papers we have counts for
7 papers
Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution
Hyungjun Kim, Jihoon Park, Changhun Lee +1
Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices…
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Jinseok Kim, Kyungsu Kim, Jae-Joon Kim
For the gradient computation across the time domain in Spiking Neural Networks (SNNs) training, two different approaches have been independently studied. The first is to compute th…
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Hyungjun Kim, Kyungsu Kim, Jinseok Kim +1
Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings. However, BNNs suffer from performance degradation mainly due t…
Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays
Hyungjun Kim, Malte Rasch, Tayfun Gokmen +5
A resistive memory device-based computing architecture is one of the promising platforms for energy-efficient Deep Neural Network (DNN) training accelerators. The key technical cha…
BitSplit-Net: Multi-bit Deep Neural Network with Bitwise Activation Function
Hyungjun Kim, Yulhwa Kim, Sungju Ryu +1
Significant computational cost and memory requirements for deep neural networks (DNNs) make it difficult to utilize DNNs in resource-constrained environments. Binary neural network…
Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators
Yulhwa Kim, Hyungjun Kim, Jae-Joon Kim
Recently, RRAM-based Binary Neural Network (BNN) hardware has been gaining interests as it requires 1-bit sense-amp only and eliminates the need for high-resolution ADC and DAC. Ho…