Personal Information
Hyeonuk Kim
research interests
International Journal Papers
Youngbeom Jung, Hyeonuk Kim, Seungkyu Choi, Jaekang Shin, Lee-Sup Kim
Energy-Efficient CNN Personalized Training by Adaptive Data Reformation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023
 
Hyeonuk Kim, Youngbeom Jung, Lee-Sup Kim
ADC-Free ReRAM-based In-Situ Accelerator for Energy-Efficient Binary Neural Networks
IEEE Transactions on Computers, 2022
 
Myeonggu Kang, Hyeonuk Kim, Hyein Shin, Jaehyeong Sim, Kyeonghan Kim, Lee-Sup Kim
S-FLASH: A NAND Flash-based Deep Neural Network Accelerator Exploiting Bit-level Sparsity
IEEE Transactions on Computers, 2022
 
Youngbeom Jung, Hyeonuk Kim, Yeongjae Choi, Lee-Sup Kim
Quantization-Error-Robust Deep Neural Network for Embedded Accelerators
IEEE Transactions on Circuits and Systems II, 2022
 
Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, Lee-Sup Kim
An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In-Situ Personalization on Smart Devices
IEEE Journal of Solid-State Circuits, 2020
 
International Conference Papers
Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, Lee-Sup Kim
A 47.4uJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices
IEEE Asian Solid-State Circuits Conference, 2019
 
Hyeonwook Wi, Hyeonuk Kim, Seungkyu Choi, Lee-Sup Kim
Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration
ACM/IEEE International Symposium on Low Power Electronics and Design, 2019
 
Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim
NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks
IEEE International Symposium on High-Performance Computer Architecture, 2019
 
Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim
A Kernel Decomposition Architecture for Binary-weight Convolutional Neural Networks
ACM/IEEE Design Automation Conference, 2017