31 citations · 72 across the 10 of their papers we have counts for
10 papers
Sparse-on-Dense: Area and Energy-Efficient Computing of Sparse Neural Networks on Dense Matrix Multiplication Accelerators
Hyunsung Yoon, Sungju Ryu, Jae-Joon Kim
As the size of Deep Neural Networks (DNNs) increases dramatically to achieve high accuracy, the DNNs require a large amount of computations and memory footprint. Pruning, which pro…
Squeezing Large-Scale Diffusion Models for Mobile
Jiwoong Choi, Minkyu Kim, Daehyun Ahn +6
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic…
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold
Changhun Lee, Hyungjun Kim, Eunhyeok Park +1
Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degrada…
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…
Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics
Hyungjun Kim, Taesu Kim, Jinseok Kim +1
Artificial Neural Network computation relies on intensive vector-matrix multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array showed a feasibility of impl…