Yiming Gan 甘一鸣

Publications

^† corresponding author  ·  ^* equal contribution

Dedicated Accelerators for Robotic Applications

• Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference

  Zheng Liu, Zeyu Guo, Zihan Liu, Anbang Wu, Han Zhao, Fangxin Liu, Zhezhi He, Yinhe Han, Jingwen Leng, Minyi Guo, Yiming Gan^†, Yu Feng.

  59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026)

• UDyr: An Adaptive Accelerator for High-Performance Humanoid Robot Control

  Yuhui Hao, Bo Yu, Shaoshan Liu, Yinhe Han, Yu Feng, Yiming Gan^†.

  59th IEEE/ACM International Symposium on Microarchitecture (MICRO 2026)

• Velkoz: Generating Accelerators for Rigid-Flexible Robots through Domain Specific High-level Synthesis

  Guoshuai Geng^*, Yuhui Hao^*, Yinhe Han, Yu Feng, Yiming Gan^†.

  ACM Transactions on Design Automation of Electronic Systems (TODAES)

• ScaleGS: Closing the Gap between Real-time 3D Gaussian Splatting and Real-time XR Rendering

  Kaiyan Chang, Yiming Gan^†, Wenlong Zhu, Kun Wang, Zhirong Chen, Yuan Cheng, Yinhe Han, Huawei Li, Ying Wang.

  ACM Transactions on Architecture and Code Optimization (TACO)

• Unified and Efficient Factor Graph Accelerator Design for Robotic Optimization

  Qiang Liu, Yihao Hua, Yuhui Hao, Bo Yu, Shaoshan Liu, Yiming Gan^†.

  ACM Transactions on Architecture and Code Optimization (TACO)

• Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation

  Yiyang Huang^*, Yuhui Hao^*, Bo Yu, Feng Yan, Yuxin Yang, Feng Min, Yinhe Han, Lin Ma, Shaoshan Liu, Qiang Liu, Yiming Gan^†.

  52nd International Symposium on Computer Architecture (ISCA 2025)

• HEX-SIM: Evaluating Multi-modal Large Language Models on Multi-chiplet NPUs

  Xinquan Lin, Haobo Xu, Yinhe Han, Yiming Gan^†.

  IEEE International Symposium on Workload Characterization (IISWC 2024)  ·  Code

• ORIANNA: An Accelerator Generation Framework for Optimization-based Robotic Applications

  Yuhui Hao^*, Yiming Gan^*, Bo Yu, Qiang Liu, Yinhe Han, Zishen Wan, Shaoshan Liu.

  29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024)

• BLITZCRANK: Factor Graph Accelerator for Motion Planning

  Yuhui Hao^*, Yiming Gan^*, Bo Yu, Qiang Liu, Shaoshan Liu, Yuhao Zhu.

  60th Design Automation Conference (DAC 2023)

• An Energy Efficient and Runtime Reconfigurable Accelerator for Robotic Localization

  Qiang Liu, Yuhui Hao, Weizhuang Liu, Bo Yu, Yiming Gan, Jie Tang, Shaoshan Liu, Yuhao Zhu.

  IEEE Transactions on Computers (IEEE TC)

• Crescent: Taming Memory Irregularities for Accelerating Deep Point Cloud Analytics

  Yu Feng, Gunnar Hammonds, Yiming Gan, Yuhao Zhu.

  49th International Symposium on Computer Architecture (ISCA 2022)

• Archytas: A Framework for Synthesizing and Dynamically Optimizing Accelerators for Robotic Navigation

  Weizhuang Liu^*, Bo Yu^*, Yiming Gan, Qiang Liu, Jie Tang, Shaoshan Liu, Yuhao Zhu.

  54th IEEE/ACM International Symposium on Microarchitecture (MICRO 2021)

• Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines

  Yiming Gan, Bo Yu, Boyuan Tian, Leimeng Xu, Wei Hu, Shaoshan Liu, Qiang Liu, Yanjun Zhang, Jie Tang, Yuhao Zhu.

  27th IEEE International Symposium on High-Performance Computer Architecture (HPCA 2021)

Robotic Applications and Algorithms

• Hydra-Nav: Object Navigation via Adaptive Dual-Process Reasoning

  Zixuan Wang, Huang Fang, Shaoan Wang, Yuanfei Luo, Heng Dong, Wei Li, Yiming Gan^†.

  43rd International Conference on Machine Learning (ICML 2026)

• KARMA: Augmenting Embodied AI Agents with Long-and-short Term Memory Systems

  Zixuan Wang, Bo Yu, Junzhe Zhao, Wenhao Sun, Sai Hou, Shuai Liang, Xing Hu, Yinhe Han, Yiming Gan^†.

  IEEE International Conference on Robotics and Automation (ICRA 2025)

• Benchmarking and Optimizing Federated Learning with Hardware-related Metrics

  Kai Pan, Yapeng Tian, Yinhe Han, Yiming Gan^†.

  35th British Machine Vision Conference (BMVC 2024)

• Low-Latency Proactive Continuous Vision

  Yiming Gan, Yuxian Qiu, Lele Chen, Jingwen Leng, Yuhao Zhu.

  29th International Conference on Parallel Architectures and Compilation Techniques (PACT 2020) Best Paper Nomination

• TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids

  Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough.

  INTERSPEECH 2020

• SVSoC: Speculative Vision Systems-on-a-Chip

  Yiming Gan, Yuxian Qiu, Jingwen Leng, Yuhao Zhu.

  IEEE Computer Architecture Letters (CAL 2019)

Reliability and Fault Tolerance

• Aphelios: A Selective Lock-step Neural Processing Unit Design

  Wenhao Sun, Yuhui Hao, Yinhe Han, Yiming Gan^†.

  35th Great Lakes Symposium on VLSI (GLSVLSI 2025) Best Paper Nomination

• KINDRED: Heterogeneous Split-Lock Architecture for Safe Autonomous Machines

  Yiming Gan, Jingwen Leng, Bo Yu, Yuhao Zhu.

  ACM Transactions on Architecture and Code Optimization (TACO)

• The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines

  Zishen Wan^*, Yiming Gan^*, Bo Yu, Shaoshan Liu, Arijit Raychowdhury, Yuhao Zhu.

  Communications of the ACM (CACM 2024)

• VPP: The Vulnerability-Proportional Protection Paradigm Towards Reliable Autonomous Machines

  Zishen Wan^*, Yiming Gan^*, Bo Yu, Arijit Raychowdhury, Yuhao Zhu.

  ISCA 2023 Workshop, 5th Domain Specific System Architecture (DOSSA-5)

• Thales: Formulating and Estimating Architectural Vulnerability Factor for DNN Accelerators

  Abhishek Tyagi, Yiming Gan, Shaoshan Liu, Bo Yu, Paul Whatmough, Yuhao Zhu.

  29th IEEE International Symposium on High-Performance Computer Architecture (HPCA 2023)

• BRAUM: Analyzing and Protecting Autonomous Machine Software Stack

  Yiming Gan, Paul Whatmough, Jingwen Leng, Bo Yu, Shaoshan Liu, Yuhao Zhu.

  33rd IEEE International Symposium on Software Reliability Engineering (ISSRE 2022)

• Ptolemy: Architecture Support for Robust Deep Learning

  Yiming Gan^*, Yuxian Qiu^*, Jingwen Leng, Minyi Guo, Yuhao Zhu.

  53rd IEEE/ACM International Symposium on Microarchitecture (MICRO 2020)

Thesis

• Improving Fault Tolerance of Computing Systems for Autonomous Machines

  Yiming Gan.

  Ph.D. Dissertation, University of Rochester, 2023

Research

I lead a research group at the Institute of Computing Technology, Chinese Academy of Sciences. We work on autonomous machines — an exciting area that may reshape the way we live. We hope to build autonomous machines that are more intelligent, more efficient, and more reliable. Our work spans new algorithms, new computer systems, and, hopefully, real robots.

AI for Chip Design

I believe the traditional chip design process is under a radical shift with the use of AI. With AI, we can move faster, while also needing to care more about the resilience of the design. Finding a way to efficiently use AI in chip design, quantify its ability, and improve that ability is extremely interesting to me.

• AI for Chip Design Arena Hiring
• Resilient AI for Chip Design

Efficient Embodied AI Systems

I think robots will have the ability to think. The goal of this direction is to enable robots to accomplish long and complex tasks in real time. There are two fundamental challenges. First, although large language models have shown the ability to accomplish long-horizon tasks, the gap between the output of language models and the real physical world is huge. Second, the scale of large models makes it impossible for robots to finish their decision chain in real time. We currently have several related projects.

• Accelerator design for vision-language-action models
• Humanoid robot control (ongoing)
• Robotic GPU (ongoing)
• Hardwired action expert accelerator (ongoing)

Reliability

My research focuses on reliability, which I believe is particularly crucial in a world increasingly populated by robots. Ensuring reliability involves making software robust against various attacks and hardware resilient to different hazards. We are currently working on two related projects.

• Reliability for embodied AI systems (ongoing)
• Software–hardware co-design for fault tolerance in wafer-scale chips Hiring
  Prerequisite: basic computer architecture and compiler courses

Team

We are a group of students and researchers building efficient and reliable computing systems for autonomous machines.

Ph.D. Students

• Guoshuai Geng — HLS for robotic applications (TODAES).
• Yuhui Hao — Accelerator design for humanoid robot control (TC, DAC'23, ASPLOS'24, ISCA'25, GLSVLSI'25, TACO'25, TODAES, MICRO'26).
• Mingyu Shi — Model-as-a-chip.
• Xuhui Qiu — RISC-V GPGPU.

Master's Students

• Yiyang Huang — Reliability and safety in embodied AI (ISCA'25).
• Zixuan Wang — Humanoid robot control (ICRA'25, ICML'26).
• Gaoyuan Liu — NPU virtualization.
• Jiale Wei — ROS 2 on RISC-V.
• Zhuomin Liu — RVV on RISC-V.

Alumni

• Wenhao Sun — Benchmarking real-time workloads (ICRA'25, GLSVLSI'25, JFR); now Ph.D. student at HKUST.
• Sai Hou — Embodied AI robot system design (ICRA'25); now Ph.D. student at Beijing Institute of Technology.
• Kai Pan — Benchmarking robotic applications (BMVC'24).
• Xinquan Lin — Multi-chiplet simulator design (IISWC'24); now Ph.D. student at ICT, CAS.

Interested in joining us? →

Prospective Students

Much of my mentoring philosophy comes from my advisor. It has worked well between us, and I hope it will work well between us too. Generally, I am looking for students who are self-motivated, love to explore new things, and love to turn their findings into real systems. The ideal mentoring relationship should be interactive: I can help you with what I know, and you can expand my horizons with what you know. I believe a good teacher should learn from their students, and a good student should teach their advisor.

What we are looking for

We welcome students from diverse backgrounds, even if your formal education did not involve extensive programming. We welcome students from areas such as physics, mathematics, and automation. It would be helpful if you are experienced in one or more of the following:

• Solid programming skills (Python, C++)
• Computer systems (e.g., computer architecture basics, FPGA experience, compiler techniques, programming language design)
• Robotic applications (e.g., localization, planning, and control)
• Computer vision (deep learning, reinforcement learning, foundation models)
• Engineering mathematics (signal processing, dynamic systems, sparsity and compression, matrix methods, optimization)

How to get in touch

I encourage you to read our research directions first, then send me an email. Please explain why your interests align with ours and how you can contribute. Attaching a CV, transcripts, and any code or papers you are proud of is always helpful.