Dr. Wenjie Wang | Storage | Best Researcher Award

Dr. Wenjie Wang | Storage | Best Researcher Award 

Dr. Wenjie Wang | Storage | Shanghai Jiao Tong University | China

Dr. Wang Wenjie, born in Zhejiang, is a distinguished researcher whose academic and professional trajectory reflects consistent excellence in the fields of computer engineering, embedded systems, and software–hardware collaborative acceleration. He began his educational journey with a degree in Mechanical Design, Manufacturing, and Automation from Shanghai Ocean University, where alongside formal studies, he independently mastered advanced computer-related knowledge including programming, embedded systems, and operating systems, while also earning recognition through numerous innovation competitions at provincial and ministerial levels. His postgraduate education at East China Normal University deepened his focus on software engineering, where he worked at the Engineering Research Center for Software and Hardware Collaborative Design, concentrating on embedded CNN acceleration through ARM+FPGA heterogeneous platforms to enable efficient deployment in resource-constrained scenarios. Currently, he is pursuing his Ph.D. at Shanghai Jiao Tong University under the supervision of Professor Jianguo Yao at the Shanghai Key Laboratory of Scalable Computing and Systems, specializing in DPU-based collaborative acceleration of NVMe storage systems with emphasis on optimizing bandwidth fairness, remote storage access, hybrid data placement, and vector similarity search.

Professional Profile: Scopus

Selected Publications

  1. ReStor: A Hardware Framework with Queue Concurrency for Optimizing Remote NVMe Storage Access in Edge Workstations – 2025 – Citations: 3

  2. ReHSS: Optimizing Adaptive Data Placement for Hybrid Storage Systems Using In-Network Processing – 2025 – Citations: 2

  3. S-CNN-ESystem: An End-to-End Embedded CNN Inference System with Low Hardware Cost and Hardware–Software Time-Balancing – 2025 – Citations: 4

  4. An Efficient and Low-Cost FPGAs-Accelerated CNN-Based Edge Intelligent Garbage Classification System on ZYNQ – 2021 – Citations: 6