Events for June
-
MHI CommNetS seminar
Mon, Jun 12, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Costas A. Courcoubetis, Singapore University of Technology and Design
Talk Title: Drivers, Riders and Service Providers: The impact of the sharing economy on Mobility
Series: CommNetS
Abstract: Joint work with S. Benjaafar and H. Bernhard.
Ride sharing, the practice of sharing a car such that more than one person travels in the car during a journey, is often heralded as a more sustainable alternative of private transportation. It is widely believed that ride sharing through sharing economy platforms will significantly reduce congestion in populated urban areas. We introduce a model in which individuals may share rides for a certain fee, paid from the rider(s) to the driver through a ride sharing platform. Collective decision making is modelled as an anonymous non-atomic game with a finite set of strategies and payoff functions affine in the individuals' types that include their utility for using private transportation and their income. We demonstrate that equilibria in this game may be represented as convex partitions of the two dimensional type space and are unique for almost all parameter combinations. With this model we study how congestion and ownership are affected through the introduction of a ride sharing platform to a population of given characteristics. In particular, we examine whether the potential reduction in congestion widely expected is actually attainable once monetary incentives are introduced that affect both the behaviour of users and the price choices of the platform.
We find that when car costs are low, casual ride sharing (P2P) will dominate the ride sharing market. When car costs are high, professional ride sharing (B2C) will dominate. Focusing on a monopolist revenue maximizing platform we encounter some paradoxical phenomena: For example, increasing car ownership costs as a measure to curb traffic volume might yield counter-intuitive outcomes: an increase in traffic volume, ownership and platform revenue coupled with a decrease in welfare. Comparing a revenue - with a welfare-maximizing platform we find that when cars are cheap the two platform objectives may be aligned. When cars are expensive, a revenue maximizing platform tends to induce an equilibrium with strictly worse welfare and strictly higher congestion compared to the welfare optimum. This suggests that in such a setting, a monopolist platform would need to be regulated more strictly to avoid socially undesirable outcomes.
Biography: Prof. Costas A Courcoubetis was born in Athens, Greece and received his Diploma (1977) from the National Technical University of Athens, Greece, in Electrical and Mechanical Engineering, his MS (1980) and PhD (1982) from the University of California, Berkeley, in Electrical Engineering and Computer Science. He was MTS at the Mathematics Research Center, Bell Laboratories, Professor in the Computer Science Department at the University of Crete, Professor in the Department of Informatics at the Athens University of Economics and Business, and since 2013 Professor in the ESD Pillar, Singapore University of Technology and Design where he heads the Initiative for the Sharing Economy and co-directs the new ST-SUTD Center for Smart Systems. His current research interests are economics and performance analysis of networks and internet technologies, sharing economy, regulation policy, smart grids and energy systems, resource sharing and auctions. Besides leading a large number of research projects in these areas he has also published over 100 papers in scientific journals and conferences. He is co-author with Richard Weber of "Pricing Communication Networks: Economics, Technology and Modeling" (Wiley, 2003).
Host: Prof. Insoon Yang
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Posted By: Annie Yu
-
Fiber Optics Manufacturing in Space (FOMS): Live Payload Demo
Fri, Jun 16, 2017 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical Engineering
Conferences, Lectures, & Seminars
Speaker: Dmitry Starodubov, FOMS Inc. and University of Southern California
Talk Title: Fiber Optics Manufacturing in Space (FOMS): Live Payload Demo
Abstract: Photonics manufacturing is taking the first steps outside our planet. The sustainable orbital manufacturing with commercially viable and profitable operation has tremendous potential for driving the space exploration industry and human expansion into outer space. FOMS Inc. team identified an opportunity of revolutionary optical fiber manufacturing in space that leads to the first commercial production on orbit. This NASA and CASIS sponsored mission is driven by strong commercial potential for manufacturing operations on board the International Space Station. The emerging Space Fiber TM product line applications are discussed. The world's first actual orbital manufacturing payload prototype will be demonstrated.
Biography: Prof. Dmitry Starodubov is a Chief Scientist of FOMS Inc. (San Diego, CA) who also recently joined USC Electrical Engineering as a visiting research faculty. Dr. Starodubov carrier path includes high profile technology positions including Technology Officer of IPG Photonics (NASDAQ: IPGP), Director of Bio- and Nano Photonics at POC and Technology Officer of USC startup D-STAR Technologies. He is an author of 23 US Patents, more than 100 publications and recipient of 2002 Photonics Excellence and 2012 Photonics Prism awards. Dr. Starodubov is a Senior OSA Member and Fellow of SPIE.
Host: Alan Willner, x04664, willner@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Posted By: Gerrielyn Ramos
-
Software-Hardware Co-Design for Efficient Neural Network Acceleration on FPGA
Fri, Jun 23, 2017 @ 10:30 AM - 11:30 PM
Ming Hsieh Department of Electrical Engineering
Conferences, Lectures, & Seminars
Speaker: Yu Wang, Tsinghua University
Talk Title: Software-Hardware Co-Design for Efficient Neural Network Acceleration on FPGA
Abstract: Artificial neural networks, efficiency compared with general-purpose processors. However, the long development period and insufficient performance of traiditional FPGA acceleration prevent it from wide utilization. We propose a complete design flow to achieve both fast deployment and high energy efficiency for accelerating neural networks on FPGA [FPGA 16, FPGA 17 best paper]. Deep compression and data quantization are employed to exploit the redundancy in algorithm and reduce both computational and memory complexity. Two architecture designs for CNN and DNN/RNN are proposed together with compilation environment. Evaluated on Xilinx Zynq 7000 and Kintex Ultrascale series FPGA with real-world neural networks, up to 15 times higher energy efficiency can be achieved compared with mobile GPU and desktop GPU. Finally, we will discuss the possibilities and trends of adopting emerging NVM technology for efficient learning systems to further improve the energy efficiency.
Biography: Yu Wang is currently a tenured Associate Professor with the Department of Electronic Engineering, Tsinghua University. He received his B.S. degree in 2002 and Ph.D. degree (with honor) in 2007 from Tsinghua University, Beijing. He has published over 150 papers in refereed journals and conferences in Design Automation and FPGA related area. His research interests include brain inspired computing, application specific hardware computing, parallel circuit analysis, and power/reliability aware system design methodology.
Host: Viktor Prasanna, prasanna@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Posted By: Kathy Kassar
