||May 3-5, 2018|
hosted by Oakland University
Rochester, Michigan, USA
|2018 IEEE INTERNATIONAL CONFERENCE on |
On Machine Learning and Classification Algorithms
Advait Madhavan received his PhD in 2016 from the Electrical and Computer Engineering Department at UC Santa Barbara. His research interests are focused on novel methods for information processing, ranging from conceptualization of high-level architectures, analog and digital circuit design as well as integration with emerging technologies and chip design. He was awarded the Micro Top Pick award in 2015. He is currently a Post-Doctoral Researcher at National Institute of Standards and Technology, Gaithersburg.
Brains, Memristors and Race Logic: A new logic for energy efficient computation
Powered by technology scaling, computing has seen a steady exponential growth in performance for almost half a century. We have grown so accustomed to computing becoming faster, cheaper, and more energy efficient, we simply assume that it will continue. Unfortunately, with Dennard scaling coming to an end, computer architects are being forced to look for inventive ways to maintain the performance gains of yesteryear, without an exponentially larger number of transistors, as well as, within a fixed energy budget.
In this talk, I will present a new kind of logic, called Race Logic, that attempts to improve both the performance and energy efficiency for certain classes of problems. Race Logic is inspired by the seemingly temporal representation of information in the brain and hence departs from the standard digital abstraction for representing information as strict 1's and 0's. Changing the information representation affects fundamental primitives of computation, the architectures that emerge from it and the novel technologies(such as resistive switches) that are synergistic with it. I will present two such architectures that benefit from Race Logic implementations and will end with results from simulations and fabricated chips that compare favorably with the state of the art.
The Automotive CPU age has passed
Professor Mark A. Steffka, has a B.S.E. – E.E., from the University of Michigan - Dearborn, and a M.S. from Indiana Wesleyan University. He has held academic appointments since 2000 as a Lecturer with the Electrical and Computer Engineering department at the University of Michigan - Dearborn, and since 2006 as an Adjunct Professor at the University of Detroit – Mercy. His university responsibilities include teaching undergraduate, graduate, and professional development courses on electrical/electronic circuit design, electromagnetic compatibility (EMC), antennas, and electronic communication systems. He is an IEEE Senior Member, a member of SAE International, and has had numerous leadership roles both within IEEE and SAE. In 2010 he was selected as an IEEE EMC Society Distinguished Lecturer, and in 2016 he was the recipient of the IEEE "Laurence G. Cumming Award" (the EMC society's highest distinction).
Mr. Steffka also has over 35 years of full time industry experience with military HF/VHF/UHF secure communications, spacecraft instrumentation, automotive electrical/electronics, and industrial electronics. Currently he is at the General Motors Global Technical Center and is a technical leader for the vehicle antennas group. Prior to this role, he was a Global Team Leader – EMC Technical Specialist with General Motors Global Propulsion Systems.
His patents include methods for electromagnetic interference reduction methods, as well as aircraft and ground vehicle antenna systems. Professor Steffka is the co-author of the book "Automotive Electromagnetic Compatibility" (2004), is the author (or co-author) of numerous technical papers presented at IEEE and SAE conference/symposiums (both as a technical session participant or invited speaker).
Knowledge-Based Perspective on Big Data-Driven Smart City Developments
The trends of urbanization and intelligent networking of different devices are leading to significant changes in our cities. A city in its complex form is an organization that is constantly in a dynamic movement due to changes in population, technologies, environmental influences or new legal regulations. Smart cities need intelligent, multi-dimensional big data analysis to support the city administration, integration and information of citizens, but also for urban planning based on recognized trends. Knowledge-based approaches using big data infrastructures enable the continuous evaluation of key indicators and target conditions of water and air quality, resource management, traffic analysis, disaster prevention and prediction, but also decision support at various levels. This talk will focus on current smart city research, knowledge management and the intelligent use of big data as a background and best practice for storing, disseminating, accessing and using the right information in the right place at the right time.