At Wisconsin Computational Intelligence Lab (WiCIL), we are exploring non-conventional computing paradigms beyond Von Neumann computing, to make future computer system faster, compacter, more intelligent, energy efficient and easier to use.
We are also interested in developing new hardware accelerators and coprocessors using FPGA and focusing on the collaborative hardware/software techniques to extend the system scaling roadmap for both legacy and emerging applications. For now, we focus on hardware/software co-design for machine learning and artificial intelligence, aiming to transform a wide spectrum of computer systems ranging from edge computing devices (IoT, Mobile) to warehouse-scale computers (Cloud and data centers). One key differentiator of our research is that we put strong emphasis on both machine learning and computer architecture aspects of research and the close interaction between them, including hardware-friendly machine learning methods and machine learning friendly system architecture. More details about our research can be found at Research and our white paper.
Our research also greatly benefits from our strong ties with leading technology companies and successful technology transfer experience.