In the past decade, Machine Learning has become one of the major opportunities to perform breakthrough cross-domain research in conventional scientific fields, through ever increasing hardware performance and abundant availability of data through ubiquitous connectivity. The embedded Signal Processing and Machine Learning (eSPML) Lab is chartered to research with two main focuses:
Machine Learning, Neural Networks and Deep Learning approaches are meanwhile becoming increasingly prominent in various computer science and circuits and systems societies, where system identification and dimensionality reduction are the challenge. The underlying idea of this lab is to bring diverse, yet complementary skillsets in these fields together to jointly achieve solution inspiring research results.
Our vision is to research and develop a new basis for paradigm shifting signal processing solutions changing the industry.
We combine machine learning and signal processing with novel integrated circuit design approaches to make leaps forward in terms of power as well as cost efficiency and to reach yet unachieved performance and capabilities of embedded solutions.