Investigating Functional Redundancies in the Context of Vehicle Automation - A Trajectory Tracking Perspective. - Torben Stolte; Tianyu Liao; Matthias Nee; Marcus Nolte; Markus Maurer (ITSC, 2018)

Investigating Cross-domain Redundancies in the Context of Vehicle Automation - A Trajectory Tracking Perspective. - Torben Stolte; Tianyu Liao; Matthias Nee; Marcus Nolte; Markus Maurer (CoRR, 2018)

Lowering Latency of Embedded Memory by Exploiting In-Cell Victim Cache Hierarchy Based on Emerging Multi-Level Memory Devices. - Juejian Wu; Tianyu Liao; Taixin Li; Yixin Xu 001; Vijaykrishnan Narayanan; Yongpan Liu; Huazhong Yang; Xueqing Li 002 (ICCAD, 2023)

A 28nm 166.9 TOPS/W x Mb/mm2 DRAM-Free QLC Compute-in-ROM Macro Supporting High Task-Level Inference Energy Efficiency for Tiny AI Edge Devices. - Ling-An Cheong; Chen Wang; Mufeng Zhou; Tianyu Liao; Mingyen Lee; Yue Ke; Wenjun Tang; Yiming Chen; Xirui Du; Yongpan Liu; Huazhong Yang; Xueqing Li 002 (A-SSCC, 2024)

Cramming More Weight Data Onto Compute-in-Memory Macros for High Task-Level Energy Efficiency Using Custom ROM With 3984-kb/mm2 Density in 65-nm CMOS. - Guodong Yin; Yiming Chen; Mufeng Zhou; Wenjun Tang; Mingyen Lee; Zekun Yang; Tianyu Liao; Xirui Du; Vijaykrishnan Narayanan; Huazhong Yang; Hongyang Jia; Yongpan Liu; et al. (IEEE J. Solid State Circuits, 2024)

DCiROM: A Fully Digital Compute-in-ROM Design Approach to High Energy Efficiency of DNN Inference at Task Level. - Tianyi Yu; Tianyu Liao; Mufeng Zhou; Xiaotian Chu; Guodong Yin; Mingyen Lee; Yongpan Liu; Huazhong Yang; Xueqing Li 002 (ASP-DAC, 2025)

Kung-Fu: An Energy-Efficient Compute-In-Memory Approach for Neural Network Inference Using Multi-Level Binary Computing Fusion. - Tianyu Liao; Yiming Chen; Wenjun Tang; Zhonghao Chen; Yu Wang 002; Huazhong Yang; Xueqing Li 002 (ISCAS, 2025)