Peng Qu

Peng Qu is an Associate Researcher in the Department of Computer Science and Technology at Tsinghua University. His research interests include computer architecture and brain-inspired computing. He has contributed to the development of a high-performance, learnable, and general spiking neural network (SNN) computing framework, supported by the National Natural Science Foundation of China. His work on a system hierarchy for brain-inspired computing was published in Nature, marking a significant contribution to the field. Additionally, he has authored several papers in reputable journals, including IEEE Transactions on Parallel and Distributed Systems and the Journal of Computer Science and Technology.

Peng Qu is a researcher at Tsinghua University focusing on brain-inspired computing, neuromorphic hardware, and high-performance computing systems. His work explores the design of brain-inspired computing systems, including neural network transformation under neuromorphic hardware constraints and the development of efficient simulation frameworks for spiking neural networks on GPU clusters. Qu also investigates cloud gaming systems, resource scheduling, and software-hardware co-design for neural networks. His research emphasizes scalable and flexible neuromorphic processors, dataflow transformation accelerators, and optimization techniques for software streaming services and large language models. His contributions span both theoretical and applied aspects of computing systems, with a focus on performance, scalability, and energy efficiency.