Fundamental problems in and applications of computer science in the STEM disciplines
Presented by Michael Niemier
This seminar will introduce participants to forthcoming changes in information processing systems, as well as fundamental issues and problems that are driving work in computer science. Today, most information processing is done via the von Neumann model where we process information by writing software (or code) to describe an algorithm that can solve a problem of interest. Code is compiled (i.e., broken down into a sequence of instructions that a microprocessor can understand). Step functionality (i.e., digital instruction encodings) and the digital data for the instructions are stored in the microprocessor’s memory, and are in turn fetched and executed, and the problem’s solution is generated. For over 30 years, MOS field effect transistors (MOSFETs) have been the mainstay of the now $400B/year semiconductor industry and are used to both process and store information on chip – typically by implementing logic and memory for a von Neumann architecture! Transistor scaling has become limited by physics, cost, and energy concerns. A replacement for the MOSFET has proven to be elusive, and industry and government sponsors are focusing on hardware research that is complemented by application-driven system research to develop new computational models to solve problems of interest. In this regard, computational models such as machine learning algorithms are already driving research that has been commercialized at companies such as Google, Apple etc., and industry is working to develop radically different computer architectures to support these models. It is essential that students are prepared for forthcoming changes with respect to how we “process information.” The seminar’s content will encompass both the hardware and devices used to process information, as well as the software and computational models used to define a solution to a problem of interest. We focus on systems that (i) are inspired by biology/the brain, (ii) can be more easily realized with new information processing technologies, and (iii) can meet the computational needs of emerging applications. Discussions will be presented in the context of (a) computer vision and biometrics, (b) information searches and the trustworthiness thereof, and (c) Moore’s Law and machine learning. Moreover, throughout the seminar we will highlight work done by local teachers from all STEM disciplines that has translated ideas from the research threads discussed above to K-12 classrooms.
About Michael Niemier
Michael T. Niemier is currently an Associate Professor at the University of Notre Dame. His research interests include designing, facilitating, benchmarking, and evaluating circuits and architectures based on emerging technologies. Currently, Niemier's research efforts look at how new information processing technologies can meet the computational needs of new application spaces such as machine learning, artificial intelligence, and improved hardware security. His research sponsors include the National Science Foundation (NSF), the Defense Advanced Research Project Agency (DARPA), the Semiconductor Research Corporation (SRC) — a consortium of leading industry members such as Intel, Samsung, IBM, etc. As such, all discussions will be framed by the current “state-of-the-art”. Niemier also oversees various NSF sponsored Research Experience for Teachers programs that expose area teachers to the above topics, and that aim to translate research ideas into K-12 classrooms. He is the recipient of an IBM Faculty Award, the Rev. Edmund P. Joyce, C.S.C. Award for Excellence in Undergraduate Teaching at the University of Notre Dame, as well as the Department of Computer Science and Engineering Teaching Award at the University of Notre Dame. Niemier has served on numerous technical program committees for design related conferences, and has chaired the emerging technologies track at DATE, DAC, and ICCAD — the leading conferences in his field. He is an Associate Editor for IEEE Transactions on Nanotechnology, as well as the ACM Journal of Emerging Technologies in Computing.