Device Non-Ideality Effects and Architecture-Aware Training in RRAM In-Memory Computing Modules. - Qiwen Wang; Yongmo Park; Wei D. Lu (ISCAS, 2021)

Device Variation Effects on Neural Network Inference Accuracy in Analog In-Memory Computing Systems. - Qiwen Wang; Yongmo Park; Wei D. Lu (Adv. Intell. Syst., 2022)

Columnar Learning Networks for Multisensory Spatiotemporal Learning. - Sangmin Yoo; Yongmo Park; Ziyu Wang; Yuting Wu; Saaketh Medepalli; Wesley Thio; Wei D. Lu (Adv. Intell. Syst., 2022)

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators. - Ziyu Wang; Yuting Wu; Yongmo Park; Sangmin Yoo; Xinxin Wang 002; Jason K. Eshraghian; Wei D. Lu (Adv. Intell. Syst., 2023)

Dramaton: A Near-DRAM Accelerator for Large Number Theoretic Transforms. - Yongmo Park; Subhankar Pal; Aporva Amarnath; Karthik Swaminathan; Wei D. Lu; Alper Buyuktosunoglu; Pradip Bose (IEEE Comput. Archit. Lett., 2024)

Side-Channel Attack Analysis on In-Memory Computing Architectures. - Ziyu Wang; Fan-Hsuan Meng; Yongmo Park; Jason K. Eshraghian; Wei D. Lu (IEEE Trans. Emerg. Top. Comput., 2024)

FHENDI: A Near-DRAM Accelerator for Compiler-Generated Fully Homomorphic Encryption Applications. - Yongmo Park; Aporva Amarnath; Subhankar Pal; Karthik Swaminathan; Alper Buyuktosunoglu; Hayim Shaul; Ehud Aharoni; Nir Drucker; Wei D. Lu; Omri Soceanu; Pradip Bose (HPCA, 2025)