Short CV

Chao Qian, M.Sc., has been working as a researcher and PhD student at the UDE's Department of Embedded Systems since April 2020. He received his bachelor's degree in Electronic Engineering at the University of Electronic Science and Technology of China in 2015 with a focus on wireless sensor networks. From 2013 to 2017, he was working in a company designing and producing wearable devices and humanoid robots. In 2020, he received his master's degree in Embedded Systems with a focus on energy-efficient embedded AI systems at the University Duisburg-Essen.

Until March 2022 he worked on the research project “KI-Sprung: LUTNet – An Energy-Efficient AI Network Based on Elementary Lookup Tables,” which was funded by the Federal Ministry of Education and Research. This project investigated how pre-trained and pre-optimized neural networks can be used to develop AI solutions that that run with high efficiency on FPGAs.

Research

His research focuses on energy-efficient Deep Learning accelerators on FPGAs, with an emphasis on developing LSTM accelerators in RTL using VHDL. At the RTL level, key methods include pipelining, operation parallelization, and efficient implementation of activation functions. At a higher level, workload-aware optimization further improves energy efficiency by reducing configuration overhead based on application workload intensity.