World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
38
Citations
6132
World Ranking
4906
National Ranking
723

Overview

Shaojun Wei is affiliated with Tsinghua University in China and has contributed extensively to the fields of computer science and engineering. Their research spans numerous subfields including electrical and electronic engineering, artificial intelligence, hardware and architecture, computer vision and pattern recognition, and computer networks and communications.

The scientist's primary research topics include advanced memory and neural computing, ferroelectric and negative capacitance devices, parallel computing and optimization techniques, advanced neural network applications, embedded systems design techniques, coding theory and cryptography, and interconnection networks and systems.

Shaojun Wei has frequently published in several key venues, including:

  • IEEE Journal of Solid-State Circuits
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • IEEE Transactions on Circuits and Systems I Regular Papers
  • IACR Transactions on Cryptographic Hardware and Embedded Systems
  • IEEE Transactions on Parallel and Distributed Systems

Notable recent publications illustrate the breadth and technical focus of their work:

  • "Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT" (2020), published in IACR Transactions on Cryptographic Hardware and Embedded Systems
  • "A 28nm 29.2TFLOPS/W BF16 and 36.5TOPS/W INT8 Reconfigurable Digital CIM Processor with Unified FP/INT Pipeline and Bitwise In-Memory Booth Multiplication for Cloud Deep Learning Acceleration" (2022), presented at the 2022 IEEE International Solid-State Circuits Conference (ISSCC)
  • "LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR" (2021), published in IEEE Transactions on Circuits and Systems I Regular Papers
  • "A Compact and High-Performance Hardware Architecture for CRYSTALS-Dilithium" (2021), published in IACR Transactions on Cryptographic Hardware and Embedded Systems
  • "ReDCIM: Reconfigurable Digital Computing-In-Memory Processor With Unified FP/INT Pipeline for Cloud AI Acceleration" (2022), published in IEEE Journal of Solid-State Circuits

The scientist has collaborated frequently with a set of co-authors, indicating an active network of research partnerships. Key collaborators include Leibo Liu, Shouyi Yin, Fengbin Tu, Jianfeng Zhu, and Yang Hu.

Best Publications

  • FP-BNN

    Shuang Liang;Shouyi Yin;Leibo Liu;Wayne Luk

  • A High Energy Efficient Reconfigurable Hybrid Neural Network Processor for Deep Learning Applications

    Shouyi Yin;Peng Ouyang;Shibin Tang;Fengbin Tu

  • A Survey of Coarse-Grained Reconfigurable Architecture and Design: Taxonomy, Challenges, and Applications

    Leibo Liu;Jianfeng Zhu;Zhaoshi Li;Yanan Lu

  • A Crop Monitoring System Based on Wireless Sensor Network

    Unknown

  • Highly Efficient Architecture of NewHope-NIST on FPGA using Low-Complexity NTT/INTT

    Neng Zhang;Bohan Yang;Chen Chen;Shouyi Yin

  • A 1.06-to-5.09 TOPS/W reconfigurable hybrid-neural-network processor for deep learning applications

    Shouyi Yin;Peng Ouyang;Shibin Tang;Fengbin Tu

  • A Multilevel Cell STT-MRAM-Based Computing In-Memory Accelerator for Binary Convolutional Neural Network

    Yu Pan;Peng Ouyang;Yinglin Zhao;Wang Kang

  • A 5.1pJ/Neuron 127.3us/Inference RNN-based Speech Recognition Processor using 16 Computing-in-Memory SRAM Macros in 65nm CMOS

    Ruiqi Guo;Yonggang Liu;Shixuan Zheng;Ssu-Yen Wu

  • Polyhedral model based mapping optimization of loop nests for CGRAs

    Dajiang Liu;Shouyi Yin;Leibo Liu;Shaojun Wei

  • RANA: towards efficient neural acceleration with refresh-optimized embedded DRAM

    Fengbin Tu;Weiwei Wu;Shouyi Yin;Leibo Liu

  • A Compact and High-Performance Hardware Architecture for CRYSTALS-Dilithium

    Cankun Zhao;Neng Zhang;Hanning Wang;Bohan Yang

  • A 141 UW, 2.46 PJ/Neuron Binarized Convolutional Neural Network Based Self-Learning Speech Recognition Processor in 28NM CMOS

    Shouyi Yin;Peng Ouyang;Shixuan Zheng;Dandan Song

  • LWRpro: An Energy-Efficient Configurable Crypto-Processor for Module-LWR

    Yihong Zhu;Min Zhu;Bohan Yang;Wenping Zhu

  • TranCIM: Full-Digital Bitline-Transpose CIM-based Sparse Transformer Accelerator With Pipeline/Parallel Reconfigurable Modes

    Unknown

  • 9.2A 28nm 12.1TOPS/W Dual-Mode CNN Processor Using Effective-Weight-Based Convolution and Error-Compensation-Based Prediction

    Huiyu Mo;Wenping Zhu;Wenjing Hu;Guangbin Wang

  • An Energy-Efficient Reconfigurable Processor for Binary-and Ternary-Weight Neural Networks With Flexible Data Bit Width

    Shouyi Yin;Peng Ouyang;Jianxun Yang;Tianyi Lu

  • Evolver: A Deep Learning Processor With On-Device Quantization–Voltage–Frequency Tuning

    Fengbin Tu;Weiwei Wu;Yang Wang;Hongjiang Chen

  • An Ultra-Low Power Binarized Convolutional Neural Network-Based Speech Recognition Processor With On-Chip Self-Learning

    Shixuan Zheng;Peng Ouyang;Dandan Song;Xiudong Li

  • Data-Flow Graph Mapping Optimization for CGRA With Deep Reinforcement Learning

    Dajiang Liu;Shouyi Yin;Guojie Luo;Jiaxing Shang

  • A High Throughput Acceleration for Hybrid Neural Networks With Efficient Resource Management on FPGA

    Shouyi Yin;Shibin Tang;Xinhan Lin;Peng Ouyang

  • An Efficient Application Mapping Approach for the Co-Optimization of Reliability, Energy, and Performance in Reconfigurable NoC Architectures

    Chen Wu;Chenchen Deng;Leibo Liu;Jie Han

  • Fast traffic sign recognition with a rotation invariant binary pattern based feature.

    Shouyi Yin;Peng Ouyang;Leibo Liu;Yike Guo

Frequent Co-Authors

Shouyi Yin
Shouyi Yin Tsinghua University
Jie Han
Jie Han University of Alberta
Weisheng Zhao
Weisheng Zhao Beihang University
Youguang Zhang
Youguang Zhang Beihang University
Sheng Zhou
Sheng Zhou Tsinghua University
Yuan Xie
Yuan Xie Hong Kong University of Science and Technology
Meng-Fan Chang
Meng-Fan Chang National Tsing Hua University
Wang Kang
Wang Kang Beihang University
Jun Li
Jun Li National University of Singapore

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Electronics and Electrical Engineering in the USA, exploring related online degrees can enhance career flexibility. Many professionals complement their technical skills with management expertise, making degrees like a project management bachelor degree an attractive option. This approach equips graduates to lead engineering projects effectively, combining technical knowledge with strategic oversight.

Those seeking faster routes to career advancement might consider programs highlighted in the fastest online project management degree list. Accelerated learning paths offer flexible schedules and condensed timelines, ideal for working professionals aiming to upskill without a long hiatus from their careers.

Additionally, for individuals who prefer focused, skills-based credentials, 6-month certificate programs that pay well provide swift entry into high-demand roles with competitive salaries. These certificates often complement engineering degrees by emphasizing practical, industry-relevant skills.

It’s also important to recognize personality fit in career planning. Many introverts thrive in engineering and technical roles that require deep focus and problem-solving. Resources like best jobs for introverts highlight how introverted professionals can excel in the field while maintaining a comfortable work style.

Best Scientists Citing Shaojun Wei

Trending Scientists