Blog

Chao Qian

Energy Efficient LSTM Cells for Embedded FPGAs

Bio:

Chao Qian, M.Sc., has been working as a researcher and Ph.D. 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. At the core of his research interests are techniques that allow artificial intelligence (AI) in reconfigurable hardware, such as FPGAs, that can operate with high energy efficiency and allow to design of energy-efficient hardware with proper software components.

 

Description of the Talk:

Chao will present his recent work about how to realize the LSTM accelerators on an embedded FPGA and how to improve the accelerator’s energy-efficiency especially.