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 broadly interested in the big problems in computer system across the full stack. For that, our research spans device, VLSI, design automation, computer architecture and related system support (OS/PL/VM), and algorithm. One key differentiator of our research is that besides modeling and simulations, we put strong emphasis on real hardware demonstration through architecting, designing and testing new prototypes, both at chip level and system level. WiCIL is the first lab in computer engineering area at University of Wisconsin-Madison that supports chip tapeout and system-level prototyping with strong support from industry. The systems that WiCIL built have achieved several key milestones, including a scalable graph analytics system ranked No. 1 on GreenGraph500 list, a deep learning system that set the world-record in performance and energy efficiency for accelerating dense convolutional neural network using FPGA. We have also taped out several new computer chips with complete system support (programming model, runtime, virtualization, API, etc.) that are built with post-CMOS nonvolatile memory technology (e.g., RRAM) integrated with silicon CMOS through monolithic 3D integration, including Liquid Silicon which won DARPA Young Faculty Award (one out of 2 in computer area and one out of 26 across all areas in science and technology nationwide, the first awardee in computer engineering and computer science at UW-Madison), and Two-Dimensional Associative Processor (2D AP) which won NSF CAREER Award, in addition to the first fabricated in-memory processing chip for search, and a variable-bit storage chip which won IBM's the highest technical achievement awards, the IBM's CEO Milestone. More details about our research can be found at Research. A research vision on the importance of interaction between machine learning and computer system can be found at our white paper (co-authored with a number of great researchers in both fields).
We are also part of one of the six SRC JUMP centers - Center for Research on Intelligent Storage and Processing-In-Memory (CRISP).
Our research also greatly benefits from our strong ties with leading technology companies and successful technology transfer experience. We would like to thank the following sponsors.