Profile

Jieyuan (Eric) Zhang

Ph.D. Student in Computer Science and Technology

School of Computer Science and Engineering, UESTC

I work on brain-inspired computing, spiking neural networks, efficient AI, and hardware-software co-design. My current research centers on efficient spiking transformers, event-driven perception, and deployable low-bit neural systems.

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Education

University of Electronic Science and Technology of China

Chengdu, Sichuan | 2021.9 - 2025.6

Bachelor of Engineering, majored in Engineering of Internet-of-Things, GPA: 3.87/4

Department of Internet-of-Things, School of Information and Communication Engineering

English Language Level: CET-4 637 · CET-6 568 · IELTS 7.0

University of Electronic Science and Technology of China

Chengdu, Sichuan | 2025.9 - Present

Major in Computer Science and Technology, School of Computer Science and Engineering

Ph.D. Student, ranked 3/14 in major-based recommendation interview.

Shenzhen Loop Area Institute

Shenzhen, Guangdong | 2026.6 - Present

Shenzhen Recommendation Program Academic Elites (First Batch)

Publications

Quantized Spike-driven Transformer

Xuerui Qiu*, Malu Zhang✉, Jieyuan Zhang*, Wenjie Wei, Honglin Cao, Junsheng Guo, Rui-Jie Zhu, Yimeng Shan, Yang Yang, Haizhou Li

OpenReview PDFCode

ICLR 2025

Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks

Jieyuan Zhang, Xiaolong Zhou, Wenjie Wei, Hanwen Liu, Qian Sun, Shuai Wang, Malu Zhang✉, Yang Yang, Haizhou Li

NeurIPS 2025

Efficient 3D Recognition with Event-driven Spike Sparse Convolution

Xuerui Qiu*, Man Yao*, Jieyuan Zhang, Yuhong Chou, Ning Qiao, Shibo Zhou, Bo Xu, Guoqi Li✉

ArXivCode

AAAI 2025

S2NN: Sub-bit Spiking Neural Networks

Wenjie Wei, Malu Zhang✉, Jieyuan Zhang, Ammar Belatreche, Yimeng Shan, Hanwen Liu, Honglin Cao, Yang Yang

ArXiv

NeurIPS 2025

FPF-SNNs: Floating-Point-Free Spiking Neural Networks

Hanwen Liu, Kexin Shi, Wenjie Wei, Jieyuan Zhang, Wenyu Chen, Malu Zhang✉, Yang Yang

IEEE TETCI

Temporal-coded Spiking Transformer

Qian Sun, Chengzhuo Lu, Wenyu Chen, Wenjie Wei, Jingya Wang, Jieyuan Zhang, Xiaoli Liu, Yalan Ye, Yang Yang, Malu Zhang✉

ACM DL

ACMMM 2025

Bipolar Self-attention for Spiking Transformers

Shuai Wang, Malu Zhang✉, Jingya Wang, Dehao Zhang, Yimeng Shan, Jieyuan Zhang, Yichen Xiao, Honglin Cao, Haonan Zhang, Zeyu Ma, Yang Yang, Haizhou Li

OpenReview PDF

NeurIPS 2025 Spotlight

QP-SNN: Quantized and Pruned Spiking Neural Networks

Wenjie Wei, Malu Zhang✉, Zijian Zhou, Ammar Belatreche, Yimeng Shan, Yu Liang, Honglin Cao, Jieyuan Zhang, Yang Yang

ArXiv

ICLR 2025

Training-Free ANN-to-SNN Conversion for High-Performance Spiking Transformer

Jingya Wang, Xin Deng, Wenjie Wei, Dehao Zhang, Shuai Wang, Qian Sun, Jieyuan Zhang, Hanwen Liu, Ning Xie, Malu Zhang✉

ArXiv

AAAI 2026

HardF-SNN: Hardware-Friendly Quantization for Spiking Neural Networks with Efficient Integer-Arithmetic-Only Inference

Hanwen Liu, Kexin Shi, Jieyuan Zhang, Yimeng Shan, Jibin Wu, Wenyu Chen, Malu Zhang✉

OpenReview PDF

AAAI 2026

Neural Dynamics Self-Attention for Spiking Transformers

Dehao Zhang, Fukai Guo, Shuai Wang, Jingya Wang, Jieyuan Zhang, Yimeng Shan, Malu Zhang✉, Yang Yang, Haizhou Li

OpenReview PDF

ICLR 2026

SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

Yimeng Shan, Zhenbang Ren, Haodi Wu, Wenjie Wei, Rui-Jie Zhu, Shuai Wang, Dehao Zhang, Yichen Xiao, Jieyuan Zhang, Kexin Shi, Jingzhinan Wang, Jason K. Eshraghian, Haicheng Qu, Jiqing Zhang✉, Malu Zhang✉, Yang Yang

ArXiv

CVPR 2026 Oral

Temporal Interaction in Spiking Transformers with Multi-Delay Mixer

Kexin Shi, Hanwen Liu, Zeyang Song, Yang Liu, Jieyuan Zhang, Shuai Wang, Jibin Wu, Malu Zhang✉, Yang Yang

ArXiv

CVPR 2026

Selected Awards

National Undergraduates Electronic Design Competition (NUEDC)

National First Prize. TI Microprocessor-Based Capacitance and Inductance Parameter Measurement Device.

Tech ReportCode

8/2023

University Student Competition Five-minute Research Presentation

National Grand Prize

8/2023

Internet+ College Student Innovation and Entrepreneurship Competition - Industry Track

Provincial Silver Award

8/2022

National College Students E-commerce Innovation, Creativity and Entrepreneurship Challenge Competition

Provincial Third Prize

6/2023

National College Students Embedded Chip and System Design Competition

Provincial Third Prize

8/2023

Research Experiences

Brain-Inspired Computing and Spiking Neural Networks

UESTC | 9/2023 - Present

Undergraduate Research Training, School of Computer Science and Engineering

  • Systematic study of SNN working principles, training algorithms, and mainstream network architectures; participated in quantization work for Spike-driven Transformer focusing on model lightweighting.
  • Proficient in using and capable of secondary development of mainstream SNN libraries, including SpikingJelly, MMEngine, MMDetection, and MMSegmentation.

Neural Network Hardware-Software Co-Design

UESTC | 12/2023 - 12/2024

Undergraduate Research Training, School of Information and Communication Engineering / School of Computer Science and Engineering

  • Implemented an FPGA-based accelerator for a mechanical fault monitoring network using Verilog HDL and Vivado as part of an ASIC course design.
  • Researched acceleration strategies to develop energy-efficient deployment methods for SNNs on edge FPGA platforms.
  • Designed high-throughput parallel CNN accelerators by optimizing data flows, encapsulating DSP resources, and implementing layer-fusion quantization.

Application and Implementation of SNN in 3D Features

CASIA | 6/2024 - Present

Research Internship, Institute of Automation, Chinese Academy of Sciences

  • Developed an energy-efficient backbone network for 3D point cloud feature extraction, combining sparse convolution with spiking voxel encoding techniques.
  • Utilize and customize mainstream 3D point cloud processing libraries such as Pointcept, OpenPCDet, and Open3D to support rapid model iteration and validation in SNNs.

Development of Large Language Models and Their Applications

CUHK-Shenzhen | 3/2025 - Present

Research Internship, The Chinese University of Hong Kong, Shenzhen

  • Collaborated with the research group on AIMO, responsible for low-bit compression and inference deployment of the model.
  • Exploring linear-attention LLMs for inter-layer sharing and contextual learning; building a lightweight RWKV architecture with inter-layer parameter sharing and adaptive early-exit schemes.

Skills & Interests

Programming LanguagesC, Python, Verilog HDL, MATLAB
Research InterestsSpiking Neural Networks, Brain-inspired Computing

Presentations

Principles and Implementation of Transformer Models

Bilibili

15k+ · 656

Neural Network Quantization 01: Concepts of Quantization and PTQ

Bilibili

1.15k · 42

Neural Network Quantization 02: A Mathematical Understanding of QAT

Bilibili

682 · 21

Academic Services

International Conference on Learning Representations (ICLR) 2026Reviewer
International Conference on Machine Learning (ICML) 2026Reviewer
IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)Reviewer
IEEE Transactions on Cognitive and Developmental Systems (TCDS)Reviewer