T-EAP: Trainable Energy-Aware Pruning for NVM-based Computing-in-Memory Architecture. - Cheng-Yang Chang; Yu-Chuan Chuang; Kuang-Chao Chou; An-Yeu Wu (AICAS, 2022)

RePAIR: A ReRAM-based Processing-in-Memory Accelerator for Indel Realignment. - Ting Wu; Chin-Fu Nien; Kuang-Chao Chou; Hsiang-Yun Cheng (DATE, 2022)

E-UPQ: Energy-Aware Unified Pruning-Quantization Framework for CIM Architecture. - Cheng-Yang Chang; Kuang-Chao Chou; Yu-Chuan Chuang; An-Yeu Wu (IEEE J. Emerg. Sel. Topics Circuits Syst., 2023)

BFP-CIM: Data-Free Quantization with Dynamic Block-Floating-Point Arithmetic for Energy-Efficient Computing-In-Memory-based Accelerator. - Cheng-Yang Chang; Chi-Tse Huang; Yu-Chuan Chuang; Kuang-Chao Chou; An-Yeu Andy Wu (ASPDAC, 2024)

BFP-CIM: Runtime Energy-Accuracy Scalable Computing-in-Memory-Based DNN Accelerator Using Dynamic Block-Floating-Point Arithmetic. - Cheng-Yang Chang; Chi-Tse Huang; Yu-Chuan Chuang; Kuang-Chao Chou; An-Yeu Wu (IEEE Trans. Circuits Syst. I Regul. Pap., 2024)