FlexLearn: Fast and Highly Efficient Brain Simulations Using Flexible On-Chip Learning. - Eunjin Baek; Hunjun Lee; Youngsok Kim; Jangwoo Kim (MICRO, 2019)

A Multi-Neural Network Acceleration Architecture. - Eunjin Baek; Dongup Kwon; Jangwoo Kim (ISCA, 2020)

DifuzzRTL: Differential Fuzz Testing to Find CPU Bugs. - Jaewon Hur; Suhwan Song; Dongup Kwon; Eunjin Baek; Jangwoo Kim; Byoungyoung Lee (SP, 2021)

An accurate and fair evaluation methodology for SNN-based inferencing with full-stack hardware design space explorations. - Hunjun Lee; Chanmyeong Kim; Seungho Lee; Eunjin Baek; Jangwoo Kim (Neurocomputing, 2021)

F4T: A Fast and Flexible FPGA-based Full-stack TCP Acceleration Framework. - Junehyuk Boo; Yujin Chung; Eunjin Baek; Seongmin Na; Changsu Kim 004; Jangwoo Kim (ISCA, 2023)

STfusion: Fast and Flexible Multi-NN Execution Using Spatio-Temporal Block Fusion and Memory Management. - Eunjin Baek; Eunbok Lee; Taehun Kang; Jangwoo Kim (IEEE Trans. Computers, 2023)