Peng Wang | AI and Automation in Architecture | Editorial Board Member

Dr. Peng Wang | AI and Automation in Architecture | Editorial Board Member

Researcher | Inspur Group Co.,Ltd | China

Peng Wang is a computer architecture researcher serving as a doctoral researcher at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, specializing in GPU rendering optimization, compiler optimization, and microarchitecture-independent performance analysis. He has contributed to multiple high-impact research projects, including the development of RayBench and RenderBench benchmark suites, GPU microarchitecture-independent characteristic profiling tools, and LLVM-based RISC-V vectorization optimizations, demonstrating strong technical leadership in benchmarking, compiler engineering, and heterogeneous computing systems. His research outputs include publications in recognized journals such as Electronics, IEEE Access, and other peer-reviewed venues, along with several patents covering GPU performance optimization, cloud game automation, and feature-analysis methodologies. He has actively supported the scientific community through peer-review service for reputable journals and international conferences, reflecting his growing influence in the fields of computer architecture and intelligent systems. His combined expertise in software-hardware co-design, GPU architecture analysis, and compiler technologies positions him as an emerging leader dedicated to advancing high-performance computing research and innovation.

Profile: ORCID

Featured Publications

1. Wang P., Qu H.L., Latency-Aware and Auto-Migrating Page Tables for ARM NUMA Servers. Electronics, 2025, 14(8), 1685.

2. Wang P., Yu Z.B., LLVM RISC-V RV32X Graphics Extension Support and Characteristics Analysis of Graphics Programs. IEEE Access, 2023, 3291920.

3. Wang P., Yu Z., RenderBench: The CPU Rendering Benchmark Suite Based on Microarchitecture-Independent Characteristics. Electronics, 2023, 12(19), 4153.

4. Wang P., Yu Z., RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis. Electronics, 2023, 12(19), 4124.

5. Wang P., Chen Y., Xing M.J., Method for Supporting RISC-V Custom Extension Instructions Based on LLVM. Comput. Syst. Appl., 2021, 31(11), Article 8347.

Peng Wang’s work advances high-performance computing by delivering optimized GPU and CPU rendering benchmark suites, compiler enhancements, and microarchitecture-independent performance tools that strengthen the reliability and efficiency of modern computing systems.He aims to drive global innovation through scalable, energy-efficient, and architecture-aware computing solutions that empower future heterogeneous computing technologies.

Shixun Wu | AI and Automation in Architecture | Best Researcher Award

Dr. Shixun Wu | AI and Automation in Architecture | Best Researcher Award

Vice Professor | Chongqing Jiaotong University | China

Dr. Shixun Wu, Vice Professor at the College of information Science and Engineering and Vice Dean of the Department of Communication Engineering at Chongqing Jiaotong University, is a specialist in wireless communication, wireless localization, and machine learning. With advanced training in applied mathematics and a doctorate in electrical and computer engineering, he has contributed extensively to intelligent transportation systems, cooperative mobile localization, Wi-Fi fingerprinting, secrecy communication, and RFID identification protocols. His work includes impactful publications in leading journals and collaborative projects that advance high precision positioning, reinforcement learning based communication strategies, and secure wireless systems. Dr. Wu has held key academic and leadership roles that support program development, research coordination, and the cultivation of emerging scholars. His professional recognition includes contributions to competitive research initiatives, involvement in editorial and review activities, and active participation in scholarly communities and technical organizations. His record reflects sustained excellence in advancing communication technologies and intelligent networked systems, positioning him as a distinguished candidate for this award. His research impact includes 380 citations, 38 publications, and an h-index of 11.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Zhang H., Xu K., Huang D., He D., Wu S., Xian G., Hybrid decision-making for intelligent high-speed train operation using boundary constraint and pre-evaluation reinforcement learning. IEEE Trans. Intell. Transp. Syst., 2024, 25(11), 17979-17992.
2. Wu S., Wang S., Xu K., Wang H., Hybrid TOA/AOA cooperative mobile localization in 4G cellular networks. IEIE Trans. Smart Process. Comput., 2013, 2(2), 77-85.
3. Wu S., Zeng X., Zhang M., Cumanan K., Waraiet A., Chu Z., Xu K., LCVAE-CNN: Indoor Wi-Fi Fingerprinting CNN positioning method based on LCVAE. IEEE Internet Things J., 2025, Accepted.
4. Zhang M., Ding X., Tang Y., Wu S., Xu K., Star-RIS assisted secrecy communication with deep reinforcement learning. IEEE Trans. Green Commun. Netw., 2024, Accepted.
5. Yang X., Wu B., Wu S., Liu X., Zhao W.G.W., Time slot detection-based M-ary tree anticollision identification protocol for RFID tags in the Internet of Things. Wirel. Commun. Mob. Comput., 2021, Article ID 6638936.

Dr. Shixun Wu’s research advances precise wireless localization, intelligent communication systems, and machine learning-driven network optimization, strengthening the foundations of next-generation connected technologies. His contributions support safer transportation, more reliable indoor positioning, and secure communication frameworks that benefit both industry and society. He envisions scalable intelligent networks that enhance global digital infrastructure and drive innovation across smart mobility and IoT systems.