Papers
7
Total Citations
116
H-Index
5
About
Yiming Gan is a leading researcher at the intersection of robotics, computer architecture, and embodied AI, whose work is defining how autonomous machines compute efficiently under real-world constraints. His core contributions lie in developing hardware-software co-design frameworks that automatically synthesize and dynamically optimize specialized accelerators for robotic workloads. Gan’s landmark paper, “Archytas,” introduced a framework for synthesizing and dynamically optimizing accelerators for robotic localization, garnering 38 citations for tackling the fundamental scalability challenge in robotic computing. His influential “Eudoxus” work (33 citations) characterized and accelerated localization in autonomous machines, directly addressing the critical need for accurate, resource-efficient localization in commercial logistic robots and self-driving cars. More recently, “ORIANNA” (20 citations) advanced the field by generating accelerators that exploit the sparse structures in optimization-based robotic algorithms, overcoming the limitations of general-purpose matrix accelerators. In 2024-2025, Gan expanded into embodied AI with “KARMA,” an innovative long-and-short term memory system that augments AI agents for complex household tasks. With over 115 total citations and a track record of industry-relevant publications, Gan is shaping the future of autonomous systems from the hardware up.
Research Focus
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