Sign-magnitude SC: getting 10X accuracy for free in stochastic computing for deep neural networks. - Aidyn Zhakatayev; Sugil Lee; Hyeon Uk Sim; Jongeun Lee (DAC, 2018)

On-chip memory optimization for high-level synthesis of multi-dimensional data on FPGA. - Daewoo Kim; Sugil Lee; Jongeun Lee (ASP-DAC, 2019)

Successive Log Quantization for Cost-Efficient Neural Networks Using Stochastic Computing. - Sugil Lee; Hyeon Uk Sim; Jooyeon Choi; Jongeun Lee (DAC, 2019)

Double MAC on a DSP: Boosting the Performance of Convolutional Neural Networks on FPGAs. - Sugil Lee; Daewoo Kim; Dong Nguyen 001; Jongeun Lee (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2019)

Learning to Predict IR Drop with Effective Training for ReRAM-based Neural Network Hardware. - Sugil Lee; Giju Jung; Mohammed E. Fouda; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (DAC, 2020)

Architecture-Accuracy Co-optimization of ReRAM-based Low-cost Neural Network Processor. - Segi Lee; Sugil Lee; Jongeun Lee; Jong-Moon Choi; Do-Wan Kwon; Seung-Kwang Hong; Kee-Won Kwon (ACM Great Lakes Symposium on VLSI, 2020)

IR-QNN Framework: An IR Drop-Aware Offline Training of Quantized Crossbar Arrays. - Mohammed E. Fouda; Sugil Lee; Jongeun Lee; Gun Hwan Kim; Fadi J. Kurdahi; Ahmed M. Eltawil (IEEE Access, 2020)

Automated Log-Scale Quantization for Low-Cost Deep Neural Networks. - Sangyun Oh; Hyeonuk Sim; Sugil Lee; Jongeun Lee (CVPR, 2021)

Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators. - Giju Jung; Mohammed E. Fouda; Sugil Lee; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (DATE, 2021)

Fast and Low-Cost Mitigation of ReRAM Variability for Deep Learning Applications. - Sugil Lee; Mohammed E. Fouda; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (ICCD, 2021)

Multi-Fidelity Nonideality Simulation and Evaluation Framework for Resistive Neuromorphic Computing. - Chenghao Quan; Mohammed E. Fouda; Sugil Lee; Jongeun Lee (IEEECONF, 2022)

Accurate Prediction of ReRAM Crossbar Performance Under I-V Nonlinearity and IR Drop. - Sugil Lee; Mohammed E. Fouda; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (ICCD, 2022)

MLogNet: A Logarithmic Quantization-Based Accelerator for Depthwise Separable Convolution. - Jooyeon Choi; Hyeonuk Sim; Sangyun Oh; Sugil Lee; Jongeun Lee (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

NTT-PIM: Row-Centric Architecture and Mapping for Efficient Number-Theoretic Transform on PIM. - Jaewoo Park; Sugil Lee; Jongeun Lee (DAC, 2023)

Offline Training-Based Mitigation of IR Drop for ReRAM-Based Deep Neural Network Accelerators. - Sugil Lee; Mohammed E. Fouda; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

Training-Free Stuck-At Fault Mitigation for ReRAM-Based Deep Learning Accelerators. - Chenghao Quan; Mohammed E. Fouda; Sugil Lee; Giju Jung; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

PyAIM: Pynq-Based Scalable Analog In-Memory Computing Prototyping Platform. - Minsang Yu; Minuk Hong; Sugil Lee; Seungsu Kim; Jongeun Lee (AICAS, 2024)

FlexInt: A New Number Format for Robust Sub-8-Bit Neural Network Inference. - Minuk Hong; Hyeonuk Sim; Sugil Lee; Jongeun Lee (ICCAD, 2024)

Mitigating the Impact of ReRAM I-V Nonlinearity and IR Drop via Fast Offline Network Training. - Sugil Lee; Mohammed E. Fouda; Chenghao Quan; Jongeun Lee; Ahmed M. Eltawil; Fadi J. Kurdahi (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2025)