Student at Institute of Microelectronics of the Chinese Academy of Sciences, China
Yang Dengtian is a promising researcher in the field of Circuit and System, currently pursuing his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences. His research interests focus on hardware-software co-optimization, object detection, and hardware acceleration, with key contributions in developing post-processing accelerators for object detection and improving micro-architecture design for GPGPU. Yang’s project experience spans from UAV object detection to the design of System on Chip (SoC) and the deployment of deep learning models on specialized hardware like NVDLA IP. His dedication to advancing technology is reflected in his published works in renowned journals. Yang is a proactive learner, often sharing his findings on blogs, contributing to the academic community’s growth. His work is poised to have a significant impact in fields such as artificial intelligence, hardware design, and computer vision.
Professional Profile
Education
Yang Dengtian began his academic journey at Xi’an Jiaotong University, where he earned his Bachelor’s degree in Electronic Science and Technology in 2020. His strong foundational knowledge in electronics laid the groundwork for his current research. In 2020, he began his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences, specializing in Circuit and System. His doctoral research has primarily focused on hardware-software co-optimization and advanced object detection systems, areas that combine his deep understanding of both electronics and cutting-edge computing techniques. Yang’s education has been integral in shaping his research pursuits, allowing him to contribute valuable insights into hardware acceleration and the optimization of machine learning systems. His academic journey is ongoing, with an expected completion of his Ph.D. in 2025.
Professional Experience
Yang has worked on several innovative projects throughout his academic career. His recent project, “Learn and Improve Vortex GPGPU,” focuses on understanding GPGPU micro-architecture design and developing improvements for performance optimization. Another notable project was the “Post-Processing Accelerator for Object Detection,” where he investigated hardware-software co-optimization methods, contributing to the development of a unified accelerator system for object detection. In 2023, Yang worked on the “SoC Building and Yolox-Nano Network Deployment Based on NVDLA IP,” where he built an SoC with NVDLA IP and deployed a Yolox-Nano model on a specialized hardware platform. Yang has also worked on solutions to reduce off-chip memory accesses for CNN inference and deployed deep learning models using Vitis-AI. These experiences, along with his publications in renowned journals, highlight his advanced technical expertise and problem-solving abilities in cutting-edge electronics and AI research.
Research Interest
Yang Dengtian’s primary research interest lies in the intersection of Circuit and System design, hardware-software co-optimization, and artificial intelligence (AI). His work focuses on developing hardware accelerators for deep learning applications, particularly in object detection and micro-architecture optimization. He is passionate about creating more efficient systems for processing large-scale data, especially in environments that require real-time processing, such as unmanned aerial vehicles (UAVs) and embedded systems. Yang’s research includes developing GPGPU micro-architectures, improving System on Chip (SoC) designs, and enhancing the deployment of deep learning models on specialized hardware, such as NVDLA IP. His research aims to bridge the gap between hardware capabilities and software needs, making AI applications more accessible and efficient. He is particularly interested in creating unified frameworks for hardware-software co-design, which could significantly advance machine learning and computer vision technologies.
Awards and Honors
Yang Dengtian’s outstanding contributions to research have been recognized through various accolades. His publication in reputable journals, such as Information and IEICE Transactions on Information and Systems, demonstrates the impact of his work in the field of hardware and software co-optimization. While still early in his career, Yang’s commitment to research excellence has already led to numerous recognitions in his academic community. He has also been acknowledged for his innovative projects in hardware acceleration for AI applications, particularly in the development of post-processing accelerators for object detection. Yang’s work is a testament to his technical expertise and his potential for future awards as his research continues to make significant strides in the fields of electronics, AI, and machine learning. Given his promising trajectory, Yang is likely to receive further honors as his doctoral studies progress and his body of work grows.
Conclusion
Yang Dengtian is undoubtedly a strong contender for the Best Researcher Award due to his innovative approach to research, technical expertise, and significant contributions to the field of hardware-software co-design and optimization. His passion for learning, combined with his publications and project experience, highlights his potential to make substantial advancements in his area of study. However, expanding his collaborations and enhancing the practical impact of his research could further solidify his status as a leading researcher in the field.
Recommendation: Yang Dengtian is highly deserving of the Best Researcher Award, with his strengths outweighing areas for improvement. His future contributions are expected to have a lasting impact in the fields of object detection, hardware acceleration, and micro-architecture design.
Publications Top Noted
- Title: Nano-carriers of combination tumor physical stimuli-responsive therapies
Authors: W Jin, C Dong, D Yang, R Zhang, T Jiang, D Wu
Journal: Current Drug Delivery
Volume & Issue: 17 (7), 577-587
Year: 2020
Cited by: 7
- Title: Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software
Authors: D Yang, L Chen, X Hao, Y Zhang
Journal: Information
Volume & Issue: 16 (1), 63
Year: 2025
Cited by: Not yet cited (as of 2025)
