- Ph.D. (2012) KTH Royal Institute of Technology, Sweden

Research Interests

•  Chips and Systems for AIoT

•  Energy-Efficient Processing and Application-Specific Processor Design

•  Embedded and Distributed Intelligence Systems

•  Brain-Inspired Computing and Neuromorphic System

Short Bio: Zhuo Zou received his Ph.D. degree in Electronic and Computer Systems from KTH Royal Institute of Technology, Sweden, in 2012. Currently, he is with Fudan University Shanghai as a Full Professor, where he is conducting research on intelligent chips and systems for AIoT. Prior to joining Fudan, he was the assistant director and a project leader at VINN iPack Excellence Center, KTH, Sweden. His current research interests include low-power circuits, energy-efficient SoC, neuromorphic computing, and their applications in AIoT and autonomous systems. Dr. Zou has also been an adjunct professor and docent at the University of Turku, Finland. He is vice chairman of IFIP WG-8.12 and a senior member of IEEE.

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My research group is primarily affiliated with Fudan University in Shanghai. Ph.D., Postdoc, and research fellows vacancies are open. 

- Introduction to Electronic System Design (BSc)

- Computer Architecture (BSc)

- Intelligent Computing Systems (MSc)

- Low-Power Integrated Circutes Design (MSc and PhD)

- VLSI Engineering (MSc and PhD)

- Embedded System and Application (MSc)

Recent & Selected Publications

•  Brain-Inspired Chips and Neuromorphic Computing

1. Y. He, et al. "CAMPRO: A CAM-Based Processing-in-Memory Processor for Hyperdimensional Computing." IEEE TCAS-I 2025.

2. F. Yang, et al. "PHENICS: A Scalable Neuromorphic FPGA Architecture for Million-Neuron Cortical Simulation With 4.6x Real-Time Acceleration." IEEE TCAS-I 2026.

3. F. Yang, et al. "CorTile: A Scalable Neuromorphic Processing Core for Cortical Simulation With Hybrid-Mode Router and TCAM." IEEE TCAS-I 2024.

4. C. Liu, et al. "A Low-Power Hybrid-Precision Neuromorphic Processor With INT8 Inference and INT16 Online Learning in 40-nm CMOS." IEEE TCAS-I 2023. 

5. H. Chu, et al. "A Neuromorphic Processing System with Spike-Driven SNN Processor for Wearable ECG Classification."  IEEE TBioCAS 2022.

6. C. Ding, et al, "A Hybrid-Mode On-Chip Router for the Large-Scale FPGA-Based Neuromorphic Platform." IEEE TCAS-I 2022.

7. D. Wang, et al. "A Memristor-Based Learning Engine for Synaptic Trace-Based Online Learning." IEEE TBioCAS 2023.

8. D. Wang, et al. "Scalable Multi-FPGA HPC Architecture for Associative Memory System." IEEE TBioCAS 2024.

9. J. Yang, et al. "A 40nm 0.05-1.4uJ/inference Sample-Wise-Adaptive Spiking Neural Network Processor with Dynamic Neuron-Pruning and Unstructured-Model-Aware Architecture." CICC 2025

10. J. Yang, et al. ."A 0.66-mm2 0.49 pJ/SOP SNN Processor with Temporal-Spatial Post-Neuron-Processing and Model-Adaptive Crossbar in 40-nm CMOS." IEEE TBioCAS 2025

11. H. Zhang, et al. "A Neuromorphic Controller with On-Chip Learning for Robot Motion Control." IEEE TCAS-II 2025. 

•  Energy-Efficient Processors and AISC

12. Z. Yang, et al. "An Always-On Event-Triggered DVS Fall Detection Processor with Precision-Adaptive Inference in 40-nm CMOSs." IEEE TCAS-I 2026.

13. A. Xiao, et al. "A 40-nm Sub-mJ/transfer HDC-SNN Hybrid Processor Enabling On-Chip Few-Shot Transfer Learning for IoT Applications." IEEE TCASAI 2026.

14. S. Tan, et al. "Towards Efficient Eye Tracking in AR/VR Devices: A Near-Eye DVS-Based Processor for Real-time Gaze Estimation." IEEE TCAS-I 2025

15. H. Ji, et al. "A Communication-Aware and Resource-Efficient NoC-based Architecture for CNN Acceleration", EEE JETCAS 2024

16. J. Huang, et al. "A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses."  IEEE TBioCAS 2024

17. B. Huang, et al. " IECA: An In-Execution Configuration CNN Accelerator with 30.55 GOPS/mm2 Area Efficiency." IEEE TCAS-I 2021
18. J. Xu, et al. "ASLog: An Area-Efficient CNN Accelerator for Per-Channel Logarithmic Post-Training Quantization." TCAS-I 2023
19. D. Bao, et al. "A Wirelessly Powered UWB RFID Sensor Tag with Time-Domain Analog-to-Information Interface." IEEE JSSC 2018

20. Z. Yu, et al. "DuoQ: A DSP Utilization-aware and Outlier-free Quantization for FPGA-based LLMs Acceleration." DAC 2025

• Edge AI Systems and AIoT Applications

21. Z. Yu, et al. "SAIndust: A Self-Aware Heterogeneous Computing Framework for Industrial Internet of Things." IEEE Internet of Things Journal, 2025

22. Y. Chen, et al. "CDL-H: Cluster-Based Decentralized Learning With Heterogeneity-Aware Strategies for Industrial Internet of Things," IEEE Internet of Things Journal, 2025

23. H. Jia, et al. "A domain-specific accelerator for ultralow latency market data distribution system" IEEE TII, 2022

24. L. Qian, et al. "MCU-Enabled Epileptic Seizure Detection System With Compressed Learning."  IEEE Internet of Things Journal, 2023

25. Y. Jin, et al. "Edge-based Collaborative Training System for Artificial Intelligence-of-Things." IEEE TII 2022

Link to my Publication List from Google Scholar and DBLP