Zishen Wan  

I am a 5th-year Ph.D. student at Georgia Tech, advised by Prof. Arijit Raychowdhury and Prof. Tushar Krishna. I also closely work with Prof. Vijay Janapa Reddi. My research interests are in computer architecture and VLSI, with a focus on designing efficient and reliable hardware and systems for autonomous machines and cognitive intelligence.

Before coming to Georgia Tech, I received M.S. from Harvard University and B.Eng. from Harbin Institute of Technology. I was a visiting student at MIT CSAIL, National Chiao Tung University, and National Tsing Hua University.

My research is supported by SRC JUMP centers CoCoSys and CBRIC, Qualcomm Fellowship, Baidu Fellowship, and CRNCH PhD Fellowship. My work has been recognized with Best Paper Awards at DAC, CAL, and SRC JUMP2.0, First Place in ACM SRC, and IEEE Micro Top Picks honorable mention. I was selected as 2023 ML and Systems Rising Star and 2024 Cyber-Physical Systems Rising Star.

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• System:
  
  • System for Resilient Autonomous Machines [ASPLOS'24a] [CACM'24] [TCAD'23] [DAC'23] [DATE'23] [ICCAD'22] [DATE'22] [DAC'21]
• Architecture:
  
  • Architecture for Neuro-Symbolic and Embodied Intelligence [HPCA'25] [ASPLOS'25a] [DAC'25a] [DAC'25b] [ISPASS'25] [ICCAD'24] [ESWEEK'24] [TCASAI'24] [ISPASS'24] [DATE'24]
  • Architecture for Efficient Autonomous Machines [ASPLOS'25b] [ASPLOS'24b] [ICRA'24] [DAC'24] [MICRO'22] [CICC'22] [ISPASS'22] [AICAS'22] [ASP-DAC'22] [CAS-M'21] [DAC'20] [CAL'20]
• Solid-State Hardware:
  
  • Chip for Autonomous Machines [JSSC'23] [ISSCC'23] and Memory-Centric Computing [ISCAS'25] [ICCAD'23] [DAC'22]

This book provides a thorough overview of the state-of-the-art FPGA-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots. Some key observations of this book has been published as a survey paper in IEEE Circuits and Systems Magazine, 2021.

This book is your gateway to the fast-paced world of AI systems through the lens of TinyML. This book aims to demystify the process of developing complete ML systems suitable for deployment - spanning key phases like data collection, model design, optimization, acceleration, security hardening, and integration. Crucial systems considerations like reliability, privacy, responsible AI, and solution validation are also explored in depth. This book is led by Prof. Vijay Janapa Reddi and resonates with Harvard TinyML course. Join us in this open-source collective effort - by the community, with the community, for the community.

This book provides a comprehensive overview of state-of-the-art embodied AI robotic systems, advancing toward artificial general intelligence. It comprises 14 chapters. Part 1 (chapters 1-2) introduces embodied AI robots background and recent developments. Part 2 (chapters 3-6) explores key systems for embodied robots, including perception, localization, planning, and control. Part 3 (chapters 7-9) deeps dive into LLMs for robotic frameworks. Part 4 (chapters 10-13) discusses efficiency, robustness, safety, and data challenges in embodied AI, along with solutions. Part 5 (chapter 14) demonstrates embodied robotic applications.

This report summarizes our recent efforts in facilitating the development of scalable, efficient, adaptive, and reliable autonomous machine computing, including automatic domain-specific SoC exploration, software-hardware co-design, and performance-efficiency-resilience co-optimization.

We propose CogSys, a characterization and co-design framework dedicated to neurosymbolic AI system acceleration, aiming to win both reasoning efficiency and scalability.

We propose ReCA, a characterization and system-architecture co-design framework dedicated to cooperative embodied AI agent system acceleration, aiming to enhance both long-horizon multi-objective planning task efficiency and system scalability.

We propose OctoCache, a software system designed to accelerate 3D occupancy mapping performance in autonomous systems. OctoCache improves mapping system update speed through three mechanisms: (1) optimization of cache memory access, (2) refinement of voxel ordering, and (3) workflow parallelization.

We propose NSFlow, an FPGA framework for efficient, scalable, and adaptive across neuro-symbolic systems. NSFlow features a design architecture generator that identifies workload data dependencies and creates dataflow architectures, as well as reconfigurable array with flexible compute units and re-organizable memory.

We propose ReaLM, an algorithm/circuit co-design framework for resileint and efficient LLM inference. ReaLM systematically characterize the fault tolerance of LLMs, and introduces a statistical algorithm-based fault tolerance algorithm and error detection circuit to enable cost-effective fault detection and mitigation for LLMs.

This paper systematically categorizes the workload characteristics of embodied agent systems and presents a benchmark suite to evaluate their task performance and system efficiency, suggests system optimization strategies to improve the performance, efficiency, and scalability of future embodied system design.

We present SCALE-Sim v3, a modular cycle-accurate simulator for systolic-array-based architectures, featuring multi-core architecture with spatio-temporal partitioning, sparsity, DRAM ramulator, precise data layout modeling, and energy and power estimation via Accelergy.

We propose MulBERRY, a multi-agent robust learning framework to enhance bit error robustness and energy efficiency for autonomous swarm systems.

We propose ORIANNA, a framework leverageing a common abstraction factor graph to generate accelerators for diverse robotic applications (e.g., manipulators, vehicles, drones) containing multiple optimization-based algorithms (e.g., localization, planning).

We characterize the inherent resilience of different compute kernels in autonomous vehicles and drones systems. We analyze the protection design landscape and propose the lightweight Vulnerable-Adaptive Protection (VAP) paradigm for resilient autonomous machines.

We analyze the neuro-symbolic workload chracteristics, and present a hardware acceleration case study for vector-symbolic architecture to improve the performance, efficiency, and scalability of neuro-symbolic computing.

We systematically categorize neuro-symbolic AI workloads, conduct workload characterizations across hardware platforms, and identify cross-layer optimization opportunites for neuro-symbolic systems.

We present a cognitive-inspired modular framework for cooperative embodied AI systems and identify the system inherent characteristics and optimization opportunities. Evaluated on long-horizon multi-objective tasks, our cross-layer optimization achieves an average 3.93x speedup in end-to-end task execution.

We present Logarithmic Posit, an adaptive and hardware-friendly datatype that dynamically adapts to DNN weight/activation distributions for efficient inference. We develop Logarithmic Posit quantization and Logarithmic Posit accelerator architecture via algorithm-hardware co-design.

We introduce RobotPerf, a benchmarking suite to evaluate robotics computing performance across a diverse range of hardware platforms. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.

We present H3DFACT, the first heterogeneous 3D integrated in-memory compute engine capable of efficiently factorizing high-dimensional holographic representations towards next-generative cognitive AI.

We analyze the computational chanllenges of integrating LLMs and neuro-symbolic architecture, and explore state-of-the-art solutions, focusing on the memory-centric computing principles at both algorithmic and hardware levels.

We propose BEERY, a robust learning framework to improve bit error robustness and energy efficiency for RL autonomous systems. BEERY enables robust low-voltage operation on UAVs, leading to high energy savings in both compute-level operation and system-level quality-of-flight.

We build a ROS-based end-to-end fault analysis framework to understand the resilience of Micro Aerial Vehicles (MAVs) system, and propose two low overhead anomaly-based transient fault detection and recovery schemes.

We propose a fully-programmable heterogeneous ARM Cortex-based SoC with an in-memory low-power RRAM-based CNN and a near-memory high-speed SRAM-based SNN in a hybrid architecture, for high-speed target identification and tracking applications.

We explore the various originations of fault sources across the computing stack of autonomous systems, and discuss the diverse fault impacts and fault mitigation techniques of different scales of autonomous systems.

We propose a machine learning-based design space exploration framework, Autopilot, that can automate the full system cyber-physical co-design for aerial robots. AutoPilot consistently outperforms general-purpose processors and specialized accelerators built for drones.

We propose a new ECC scheme for hard and soft errors in foundry RRAM-based Compute-In-Memory chip. We demonstrate single, double, and triple error correction offering up to 16,000× reduction in bit error rate, while consuming only 29.1% area and 26.3% power overhead.

We present the cross-layer robotic computing stack, illustrate the current progress and key design techniques. We summarize and highlight the challenges, research opportunities, and roadmap for the next-generation FPGA-based robotic computing systems.

We present an energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization tasks. We exploit SLAM-specific data locality, sparsity, reuse, and parallelism, and achieve >5x performance improvement over state-of-the-art.

We present a bottleneck analysis tool, Skyline, for designing compute systems for autonomous Unmanned Aerial Vehicles (UAV). The tool provides insights by exploiting the fundamental relationships between various components in the autonomous UAV such as sensor, compute, body dynamics.

We characterize the hardware transient fault impact on federated reinforcement learning system, a swarm intelligence paradigm in autonomous machines. We further propose application-aware cost-effective fault detection and mitigation scheme to enable autonomy reliability.

We present a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, with an emphasis on mixed-signal circuit, neuro-inspired computing, benchmarking, software infrastructure, and algorithm-hardware co-design.

We evaluate the resilience of learning-based navigation systems to transient and permanent hardware faults. We further propose two efficient fault mitigation techniques for both RL training and inference.

We provide an overview of recent work on FPGA-based robotic accelerators. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications.

We present a real-time and energy-efficient ORB (Oriented-Fast and Rotated-BRIEF) based visual system on FPGAs. Compared to Nvidia TX1 and Intel i7 CPU, our FPGA-based implementation achieves 5.6x and 3.4x speedup, as well as 3.0x and 34.6x power reduction, respectively.

We present an algorithm-hardware co-design centered around a novel floating-point inspired number format, which can achieve higher inference accuracies and lower per-operation energy compared to NVDLA-like PE.

We introduce a roofline-like model to understand the role of computing in aerial autonomous machines. The model provides insights by exploiting the fundamental relationships between various components in an aerial robot, such as sensor framerate, compute performance, and body dynamics.

• 2025 Baidu Fellowship (10 awardees worldwide)
• 2025 Best Poster Award, DARPA SRC JUMP2.0 Center for Co-Design of Cognitive Systems (CoCoSys) Annual Review
• 2024 Cyber-Physical Systems Rising Star
• 2024 Best Paper Award, DARPA SRC JUMP 2.0 Program
• 2024 Best Presentation Award, Semiconductor Research Corporation (SRC) TECHCON
• 2024 Best Poster Award, DARPA SRC JUMP2.0 Center for Co-Design of Cognitive Systems (CoCoSys) Annual Review
• 2024 3rd Place, ACM/SIGMICRO Student Research Competition, International Symposium on Microarchitecture (MICRO)
• 2024 Spotlight Research Scholar, DARPA SRC JUMP2.0 Center for Co-Design of Cognitive Systems (CoCoSys)
• 2023 Machine Learning and Systems Rising Star
• 2023 IEEE Micro Top Picks, Honorable Mention ("in recognition of the most significant research papers in computer architecture")
• 2023 Best Paper Award, Robotics Benchmarking Workshop, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 2023 Best Poster Award, IBM IEEE AI Compute Symposium
• 2023 Roger P. Webb Graduate Research Assistant Excellence Award, Georgia Tech
• 2022 1st Place, ACM/SIGBED Student Research Competition
• 2022 3rd Place, ACM/SIGDA Student Research Competition (declined)
• 2022 Qualcomm Fellowship
• 2022 Young Fellow, IEEE/ACM Design Automation Conference (DAC)
• 2022 CRNCH PhD Fellowship, Center for Research into Novel Computing Hierarchies, Georgia Tech
• 2021 ACM SIGDA Research Highlights Nominee
• 2021 Young Fellow, IEEE/ACM Design Automation Conference (DAC)
• 2021 Best Presentation Award, Young Fellow Program at IEEE/ACM Design Automation Conference (DAC)
• 2020 Best Paper Award, IEEE/ACM Design Automation Conference (DAC)
• 2020 Best Paper Award, IEEE Computer Architecture Letters (CAL)
• Student Travel Award: HPCA'25, ISSCC'24, MICRO'24, MLSys'24, ISPASS'24, ASPLOS'24, ISCA'23, MLSys'23

• "Tailored Computing: Domain-Specific Architecture for Embodied Autonomous Machines"
- 03/2025 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA
- 02/2025 Invited Talk, UIUC Coordinated Science Laboratory (CSL), Champaign, IL
- 01/2025 Seminar Talk, University of Washington (Host: Prof. Ang Li), Seattle, WA
- 12/2024 Seminar Talk, Institute of Computing Technology, Chinese Academy of Sciences (Host: Prof. Yunji Chen), Online

• "Demystifying NeuroSymbolic AI via Workload Characterization and Software-Hardware Co-Design"
- 04/2025 Google (Host: Dr. Suvinay Subramanian), Online
- 03/2025 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA
- 01/2025 Guest Lecture, Georgia Tech ECE8893 Parallel Programming for FPGAs (Host: Prof. Callie Hao), Atlanta, GA
- 01/2025 Georgia Tech Computer Architecture Research Seminar (Arch-Whisky), Atlanta, GA
- 11/2024 ACM Student Research Competition, International Symposium on Microarchitecture (MICRO), Austin, TX
- 08/2024 Invited Talk, University of Minnesota, Twin Cities (Host: Prof. Katie Zhao), Minneapolis, MN
- 05/2024 Young Professional Symposium, Conference on Machine Learning and Systems (MLSys), Santa Clara, CA
- 05/2024 International Workshop on Neuro-symbolic Systems (NeuS), UC Berkeley, Berkeley, CA
- 03/2024 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA
- 09/2023 Guest Lecture, EE6900 Neuromorphic Computing (Host: Prof. Yan Fang), Atlanta, GA
- 05/2023 Georgia Tech 3D Systems Packaging Research Center Spring Meeting, Atlanta, GA
- 05/2023 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA

• "System-Architecture-Technology Cross-Layer Design for Autonomous and Embodied Intelligence"
- 11/2024 Invited Talk, Harvard University Nano-Design Research Group (Host: Prof. Gage Hills), Cambridge, MA
- 08/2024 Invited Talk, Lawrence Livermore National Laboratory (Host: Dr. Kshitij Bhardwaj), Livermore, CA

• "Intelligence in Robotic Computing: Exploring Agile Design Flows for Efficient and Resilient Autonomous Systems"
- 11/2024 Invited Talk, University of Central Florida Computer Architecture Seminar (Host: Prof. Di Wu), Orlando, FL
- 11/2024 Invited Talk, MICRO Workshop on Robotics Acceleration with Computing Hardware, Austin, TX
- 09/2024 ESWEEK (Embedded Systems Week) PhD Forum, Raleigh, NC
- 05/2024 Cyber-Physical System Rising Star Workshop, University of Virginia, Charlottesville, VA
- 05/2024 CoCoSys (Center for the Co-Design of Cognitive Systems) Liaison Meeting, DARPA SRC JUMP 2.0, Atlanta, GA
- 02/2024 CRIDC (Career, Research, and Innovation Development Conference), Atlanta, GA
- 02/2024 Georgia Tech Computer Architecture Research Seminar (Arch-Whisky), Atlanta, GA
- 11/2023 6th IBM AI Compute Symposium, IBM T.J. Watson Research Center, Yorktown Heights, NY
- 08/2023 ML and Systems Rising Stars Workshop, Google, Mountain View, CA
- 05/2023 Georgia Tech Chips Day, Atlanta, GA
- 03/2023 Georgia Tech EIC Lab (Host: Prof. Celine Lin), Atlanta, GA
- 02/2023 CRNCH (Center for Research into Novel Computing Hierarchies) Annual Summit, Atlanta, GA
- 11/2022 ACM Student Research Competition at ICCAD, San Diego, CA

• "Efficient Algorithm-Hardware Co-Design for Autonomous Machine Computing"
- 09/2023 Georgia Tech Computer Architecture Research Seminar (Arch-Whisky ), Atlanta, GA
- 02/2023 CRIDC (Career, Research, and Innovation Development Conference), Atlanta, GA
- 10/2022 5th IBM AI Compute Symposium, IBM T.J. Watson Research Center, Yorktown Heights, NY
- 10/2022 CBRIC (Research Center for Brain-Inspired Computing) Annual Summit, DARPA JUMP SRC, Purdue University, IN
- 03/2022 Guest Lecture, Georgia Tech ECE8893 Parallel Programming for FPGAs (Host: Prof. Callie Hao), Atlanta, GA
- 02/2022 CRNCH (Center for Research into Novel Computing Hierarchies) Annual Summit, Online
• "Enabling Reliable and Safe Autonomous Systems"
- 03/2024 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA
- 02/2024 CRNCH (Center for Research into Novel Computing Hierarchies) Annual Summit, Atlanta, GA
- 05/2023 CoCoSys (Center for the Co-Design of Cognitive Systems) Annual Summit, DARPA SRC JUMP 2.0, Atlanta, GA
- 11/2022 ACM Student Research Competition at ESWEEK, Online
- 06/2022 COMPSAC plenary panel 'Reliability of Autonomous Machines', Online
- 10/2021 CBRIC (Center for Brain-Inspired Computing) Annual Summit, DARPA JUMP SRC, Online
- 08/2021 CBRIC (Center for Brain-Inspired Computing) Industry Talk, DARPA JUMP SRC, Online
- 07/2020 Harvard Architecture, Circuits and Compilers Lab, Online

• Conference Reviewer: MLSys'25, ICRA'25, Arch4EAI@ISCA'25, SCOPE@ICLR'25, CAV@ASPLOS'24, DAC'24, DAC'23, ESWEEK'23, IJCAI'23, NPC'22.
• Journal Reviewer: IEEE JSSC, IEEE TCAD, IEEE TCAS-I, IEEE TBioCAS, IEEE JETCAS, IEEE Micro, IEEE Internet of Things Journal, IEEE TIM, ACM JATS, ACM TCPS.
• Artifact Evaluation Committee: HPCA'25, ISCA'24, MICRO'23, ISCA'23, ASPLOS'23, MLSys'23, MICRO'22, ASPLOS'22, IISWC'22.
• Workshop & Special Session Oragnizer: ICCAD'24, ESWEEK'24.
• Working Group: Co-found MLCommons (MLPerf) Resilience and Robustness Research Working Group.
• Panelist: COMPSAC'22.
• Outreach activity: ISSCC'24 News and Media Team, Steering Committee of Computer Architecture Student Association (CASA), Steering Committee of IEEE Entrepreneurship China, IISWC'19 Volunteer.

• DARPA SRC News: SRC Highlights: CoCoSys Featured in Fortune (01/2025)
• MIT Technology Review News: New Adaptive Protection Paradigm to Improve the Reliability of Robot Computing Systems (01/2025)
• Fortune News: Generative AI can’t shake its reliability problem, some say ‘neurosymbolic AI’ is the answer (12/2024)
• CoCoSys News: Zishen Wan: Research Scholar Spotlight from DARPAR SRC JUMP2.0 Program (12/2024)
• TechXplore News: Balancing cost and reliability in autonomous machine design (10/2024)
• Georgia Tech News: ECE Students Take Home Top Honors at TECHCON 2024 (10/2024)
• ACM News: Hallucination vs Creativity, Public Digital Currencies, and Reliable Autonomous Machines (09/2024)
• TechSpot News: Number Representations in Computer Hardware: Fundamentals Matter (06/2024)
• Georgia Tech News: ECE Benchmarking Making Major Advances in Machine Learning (04/2024)
• Georgia Tech News: Wan Recognized for Energy-Saving Research on Autonomous Systems (01/2024)
• Georgia Tech News: The Year in Artificial Intelligence and Machine Learning (12/2023)
• Semiconductor Engineering News: Scalable And Compact Multi-Bit CAM Designs Using FeFETs (10/2023)
• Georgia Tech News: Wan Selected as Machine Learning and Systems Rising Star (09/2023)
• RobotReport News: RobotPerf Benchmarks compare robotics computing performance (09/2023)
• Georgia Tech News: Celebrating ISCA’s 50th: Georgia Tech's Contributions, Impact, and Reflections on 50 Years of Computer Architecture Innovation (07/2023)
• Georgia Tech News: Wan Wins Computing Machinery Student Research Competition (12/2022)
• Google AI Blog: Quantization for Fast and Environmentally Sustainable Reinforcement Learning (09/2022)
• MarkTechPost News: A Novel Reinforcement Learning Training Paradigm to Speed Up Actor-Learner Distributed RL Training (09/2022)
• Georgia Tech News: Wan Selected for IEEE/ACM DAC Honors (01/2022)

• Sports: I like table tennis, soccer, swimming, hiking, and jogging. I'm a member of Georgia Tech Table Tennis Association.
• Arts: I like calligraphy and have practiced more than 10 years. My undergrad Calculus class notes was awarded 'The Most Beautiful Class Note' and permanently collected and displayed by Harbin Institute of Technology university museum.

Last Update: Apr. 2025

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