Carnegie Mellon University
Electrical and Computer Engineering
5000 Forbes Avenue
Pittsburgy, PA 15213-3890
United States of America
Just as there are frictional losses in moving a weight on a surface, there are also frictional losses in moving information on a substrate. This "information-friction" has received little attention within both theoretical computer science and information theory. But it is important in both! I will discuss our work at the intersection of both fields: information-frictional losses in the circuitry at the transmitter and the receiver of a communication system.
From a theoretical perspective, I will show how accounting for these losses leads to a novel understanding of *total* energy consumed in a communication system that goes beyond the transmit-power-centric Shannon theory. For instance, we show that approaching the Shannon-limit on transmit power is fundamentally accompanied with increasing amounts of power in the encoding/decoding circuitry (in both circuit gates and circuit wires). Thus the total-power-minimizing strategies must operate far from the Shannon limit.
From a practical perspective, our early results -- that rely on circuit simulations for power consumption estimation -- show that novel total-power-optimizing strategies can lead to substantial power reductions in short-distance communication systems. I will discuss our work on two applications (i) millimeter wave wireless; and (ii) data-center Ethernet.
Bio: Pulkit Grover (Ph.D. UC Berkeley'10, B.Tech.'03, M.Tech.'05 IIT Kanpur) is an assistant professor at CMU. He is interested in interdisciplinary research directed towards developing a science of information for making decentralized systems (from low-power communication systems to large control systems) energy-efficient and stable. He is the recipient of the best student paper award at the IEEE Conference in Decision and Control (CDC) 2010; and the 2012 Leonard G. Abraham best paper award from the IEEE Communications Society for his work on energy-efficient communication. For his dissertation research, he received the 2011 Eli Jury Award from the Department of EECS at UC Berkeley.