• 2005-2008: Newcastle University (UK), Integrated Circuit & System Design, Doctoral Degree (Full-time)"
• 2000-2004: University of Electronic Science and Technology of China, Communication Engineering & Microelectronics, Dual Bachelor Degree

Work Experience：

• 2017-Present: School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Professor
• 2014-2017: ASTAR Institute of Microelectronics (IME) Singapore, Research Scientist II & PhD Supervisor
• 2011-2014: ASTAR Institute of Microelectronics (IME) Singapore, Research Scientist I & PhD Supervisor
• 2008-2011: IMEC Netherlands, Researcher & Master’s Supervisor

Jun Zhou is currently a Professor at University of Electronic Science and Technology of China. He spent nearly 10 years working at IMEC (The Netherlands) and A*STAR IME (Singapore), as the technical lead for intelligent processor research. His main research interests cover low-power AI processor design, co-design of AI algorithms and processors, and related disciplines. He has published more than 150 papers in prestigious conferences and journals such as ISSCC, JSSC, HPCA and DAC. He has designed and taped out multiple world-record low-power edge AI processors for visual perception, acoustic sensing, wearable healthcare and other application scenarios. His has received several IEEE best paper Awards and the Wu Wenjun Artificial Intelligence Technology Invention Award. He serves as a TPC member of the Digital Architectures & Systems (DSA) Subcommittee for ISSCC (the world top conference in IC design), the Chair of the Digital Circuits & Systems (DCS) Subcommittee for A-SSCC, the Co-TPC Chair of AICAS 2026 (the flagship conference in AI circuits & systems design). He has also served as Associate Editor/Guest Editor for several prestigious journals including JSSC, TBioCAS and TVLSI.

1. R. Mao, A. Shen, L. Tang, J. Zhou*, “CREST: An Efficient Conjointly-trained Spike-driven Framework for Event-based Object Detection Exploiting Spatiotemporal Dynamics”, AAAI Conference on Artificial Intelligence (AAAI), 2025.

2. J. Xiao, X. Zhang, S. Zhu, Z. Yang, M. Du, C. Ji, Y. Long, X. Chen, X. Miao, L. Zhou, L. Chang, S. Liu, J. Zhou*, “KASP: A 97.8% 10-Keyword Accuracy and 1.68μJ/Classification Keyword Spotting and Speaker Verification Processor using Adaptive Beamforming and Progressive Wake-Up,” IEEE International Solid-State Circuits Conference (ISSCC), 2024.

3. R. Mao, L. Tang, X. Yuan, Ye Liu, J. Zhou∗, “Stellar: Energy-efficient and low-latency snn algorithm and hardware co-design with spatiotemporal computation”, IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2024.

4. R. Mao, L. Tang, Z. Xia, Z. Zhang, A. Shen, Y. Long, J. Guo, Y. He, L. Zhang, S. Wang, L. Zhou, L. Chang, S. Liu, J. Zhou*, FSNAP: An Ultra-Energy-Efficient Few-Spikes-Neuron based Reconfigurable SNN Processor Enabling Unified On-Chip Learning and Accuracy-Driven Adaptive Time-Window Tuning, IEEE Symposium on VLSI Circuits (VLSIC), 2024

5. J. Liu, X. Liu, X. Wang, Z. Xie, Z. Zhong, J. Fan, H. Qiu, Y. Xu, H. Qin, Y. Long, Y. Zhou, Z. Shen, L. Zhou, L. Chang, S. Liu, S. Lin, C. Wang, J. Zhou*, “A High Accuracy and Energy-Efficient Zero-Shot-Retraining Seizure Detection Processor with Hybrid-Feature-Driven Adaptive Processing and Learning-Based Adaptive Channel Selection,” IEEE International Solid-State Circuits Conference (ISSCC), 2024.

6. Y. Gong, T. Zhang, H. Guo, X. Liu, J. Zheng, H. Wu, C. Jia, L. Que, L. Zhou, L. Chang, J. Zhou*, "DL-VOPU: An Energy-Efficient Domain-Specific Deep Learning based Visual Object Processing Unit supporting Multi-Scale Semantic Feature Extraction for Mobile Object Detection/Tracking Applications," 2023 IEEE International Solid-State Circuits Conference (ISSCC), 2023.

7. J. Liu, Z. Xie, X. Wang, X. Liu, X. Jiao, J. Fan, H. Qin, C. Guo, J. Xiao, J. Zhou*, “BioWAP: A Reconfigurable Biomedical AI Processor with Adaptive Processing for Co-Optimized Accuracy and Energy Efficiency”, IEEE Custom Integrated Circuits Conference (CICC), 2024.

8. J. Liu, Z. Zhong, Y. Zhou, H. Qiu, J. Xiao, J. Fan, Z. Zhang, S. Li, Y. Xu, S. Yang, W. Shan, S. Lin, L. Chang, J. Zhou*, "An energy-efficient seizure detection processor using event-driven multi-stage CNN classification and segmented data processing with adaptive channel selection," ACM/IEEE Design Automation Conference (DAC), 2022.

9. J. Liu, J. Xiao, J. Fan, Q. Liu, Z. Zhu, S. Li, Z. Zhang, S. Yang, W. Shan, S. Lin, L. Chang, L. Zhou, J. Zhou*, "An Energy-Efficient Cardiac Arrhythmia Classification Processor using Heartbeat Difference based Classification and Event-Driven Neural Network Computation with Adaptive Wake-Up," 2022 IEEE Custom Integrated Circuits Conference (CICC), 2022.

10. J. Liu, Z. Zhu, Y. Zhou, N. Wang, G. Dai, Q. Liu, J. Xiao, Y. Xie, Z. Zhong, H. Liu, L. Chang, J. Zhou*, "BioAIP: A Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Intelligent Health Monitoring," 2021 IEEE International Solid- State Circuits Conference (ISSCC), 2021.

11. D. Li, R. Tian, H. Jia, W. Shao, X. Liu, Z. Chen, Z. Xu, X. Li,  S. Xu, Z. Xie, J. Jian, X. Wang, L. Tao, X. Wu, Z. Cheng, Z. Dong, H. Liu*, Y. Yang*, J. Zhou*, Tian-Ling Ren*, “Motion-unrestricted dynamic electrocardiogram system utilizing imperceptible electronics”, Nature Communications, vol. 16, no. 1, pp. 3259, 2025.

12. T. Zhang, X. Yuan, X. Zhao, H. Wei, L. Jin, J. Zeng, C. Shi, L. Zhuang, Y. Long, J. Zhou*, “PRADO: A Low-Latency and Energy-Efficient 6DoF Pose Refinement Accelerator With Domain-Specific Explorations”, IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2025.

13. Y. Liu, K. Huang, N. Zhao, Y. Li, X. Qi, R. Mao, S. Hao, J. Li, Z. Huang, M. Yang, S. Li, A. Hu, L. Zhou, Y. Long, S. Liu, J. Zhou*, “VSLAM-BA: Algorithm and Hardware Co-Design for High Performance and Energy-Efficient Visual SLAM Backend Hardware Accelerator”, IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2025.

14. Y. Liu, S. Hao, X. Qi, K. Huang, M. Yang, Z. Huang, Y. Li, L. Chang, L. Zhou, Y. Long, J. Zhou*, “An Ultra-High Performance and Scalable Optical Flow Hardware Accelerator Based on FPGA for Autonomous Driving”, IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2025.

15. R. Mao, L. Tang, Z. Zhang, Y. Lu, Y. Long, J. Guo, L. Zhou, L. Chang, S. Liu, T. Kim, J. Zhou*, “FSNAP: An Ultra-Energy-Efficient Reconfigurable Few-Spikes-Neuron-Based SNN Processor Supporting Unified On-Chip Learning and Adaptive Time-Window Tuning”, IEEE Journal of Solid-State Circuits (JSSC), 2025.

16. J. Liu, X. Liu, C. Guo, X. Wang, Z. Zhong, J. Fan, H. Qiu, Z. Xie, Y. Xu, H. Qin, Y. Long, Y. Zhou, Z. Shen, L. Zhou, L. Chang, S. Liu, S. Lin, C. Wang, J. Zhou*, "A High Accuracy and Ultra-Energy-Efficient Zero-Shot-Retraining Seizure Detection Processor," IEEE Journal of Solid-State Circuits (JSSC), vol. 59, no. 11, pp. 3549-3565, Nov. 2024.

17. L. Peng, J. Yang, Z. Chen, L. Yan, X. Jiao, J. Xiao, L. Zhou, L. Chang, Y. Long, J. Zhou*, "A High Accuracy and Low Power CNN-Based Environmental Sound Classification Processor," in IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), pp. 1-12, Aug. 2023.

18. Y. Gong, H. Guo, X. Liu, J. Zheng, T. Zhang, L. Que, C. Jia, G. Ou, X. Jiao, Z. Liu, L. Chang, L. Zhou, J. Zhou*, "An Energy-Efficient Visual Object Tracking Processor Exploiting Domain-Specific Features," in IEEE Transactions on Circuits and Systems II (TCAS-II), Early Access, 2023.

19. J. Liu, J. Fan, Z. Zhong, H. Qiu, J. Xiao, Y. Zhou,  Z. Zhu, G. Dai, N. Wang, Q. Liu, Y. Xie,  H. Liu, L. Chang, J. Zhou*. "An Ultra-low Power Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Wearable Intelligent Health Monitoring," in IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), pp. 1-16, Aug. 2023.

20. Y. Liu, F. Wu, N. Zhao, Q. Zhang, W. Wang, Y. Yang, X. Li, S. Li, Z. Huang, J. Zhou*. "NVP: A Flexible and Efficient Processor Architecture for Accelerating Diverse Computer Vision Tasks including DNN," in IEEE Transactions on Circuits and Systems II: Express Briefs (TCAS-II), vol. 70, no. 1, pp. 271-275, Jan. 2023.