Shiqi Lian

University of Chinese Academy of Sciences, Chinese Academy of Sciences, Institute of Computing Technology

Papers

Total Citations

H-Index

About

Shiqi Lian is a pioneering researcher in robotics acceleration, specializing in hardware-software co-design to overcome the computational bottlenecks of real-time robot motion planning. His major contributions center on the "Dadu" family of accelerators, which dramatically speed up collision detection and inverse kinematics—two of the most time- and energy-intensive operations in robotics. His most cited work, "Dadu-P" (2018, 24 citations), introduces a scalable accelerator that enables valid motion planning in dynamic environments, a feat previously unattainable with general-purpose processors. Lian’s research addresses the critical need for real-time performance in robotics, where traditional CPUs fall short. His 2017 "Dadu" paper (18 citations) targets the inverse kinematics problem, essential for robot walking and balancing, while his 2020 work on TCAM-based acceleration (3 citations) explores novel memory architectures to accelerate path search and collision detection. Collectively, his DaDu series (2020, 2 citations) provides a comprehensive analysis of robotics accelerators, highlighting their advantages and limitations. With a total of 67 citations across his top papers, Lian’s work is foundational for students and researchers seeking to push the boundaries of real-time robotic control.