TUTORIAL - Track 2

Channel Coding Methods for Emerging Data Storage and Memory Systems: Opportunities to Innovate Beyond the Hamming Metric

Lara Dolecek and Andrew Jiang

Sunday June 29, 2014
13:30 - 16:30
Room: 316B


Recent explosive growth in data generation has placed an unprecedented demand on storage technologies to be ultra fast, reliable and cost-effective. Novel nonvolatile-memory and data-storage architectures are being actively investigated to support future large-scale information systems. However, currently available mathematical solutions have hit a performance wall: existing approaches are designed for simpler (symmetric) channels that are typically governed by the optimization for the Hamming distance, and do not match the needs of new storage technologies where the data must be packed as densely as possible on increasingly adverse mediums. The resulting performance provisioning is not only suboptimal in terms of fundamental information-theoretic laws, but it also directly increases the cost of a storage system. Given the potential for an immediate impact in modern storage systems, it is now of great interest to innovate in the area of channel coding beyond the conventional symmetric error correction and the associated Hamming metric.

In this tutorial, we will overview several recent exciting research developments in the area of coding for non-volatile memories and data storage. These results span both algebraic and graph-based channel codes and include rewriting codes, rank modulation, graded-bit-error codes and constrained coding for inter-cell interference. The new metrics of interest include Kendal-Tau distance, L_1 distance, and Ulam metric, among others.

We will first present the overview of the channel models for non-volatile memories, including flash and phase-change memories, and highlight their key properties, such as operational asymmetry and spatio-temporal variability. We will then describe coding-theoretic methods for the correction of asymmetric and limited-magnitude errors and the mitigation of inter-cell interference, followed by discussions of codes for rewriting data and the rank modulation schemes. Several suggestions for future investigation that would be of interest to ISIT audience and that would have cross-disciplinary impact will round up the last portion of this tutorial.


Lara Dolecek is an Assistant Professor with the Electrical Engineering Department at the University of California, Los Angeles (UCLA). She holds a B.S. (with honors), M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences, as well as an M.A. degree in Statistics, all from the University of California, Berkeley. She received the 2007 David J. Sakrison Memorial Prize for the most outstanding doctoral research in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Prior to joining UCLA, she was a postdoctoral researcher with the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology. She received Northrop Grumman Excellence in Teaching Award (2013), Intel Early Career Faculty Award (2013), University of California Faculty Development Award (2013), Okawa Research Grant (2013), NSF CAREER Award (2012), and Hellman Fellowship Award (2011). She is an Associate Editor for Coding Theory for IEEE Transactions on Communications and for IEEE Communication Letters and is the lead guest editor for IEEE JSAC special issue on emerging data storage. Her research interests span coding and information theory, graphical models, statistical algorithms, and computational methods, with applications to emerging systems for data storage, processing, and communication.

Anxiao (Andrew) Jiang is an associate professor in the Computer Science and Engineering Department and the Electrical and Computer Engineering Department of Texas A&M University. He received his Ph.D. and M.S. degrees in Electrical Engineering at the California Institute of Technology in Pasadena, California and his B.E. degree in Electronic Engineering at Tsinghua University in Beijing, China.

He was a recipient of the National Science Foundation CAREER Award in 2008 for his exploratory work on coding for flash memories. He was also a recipient of the 2009 IEEE Communications Society Best Paper Award on Signal Processing and Coding for Data Storage. His research interests include data storage, information theory and algorithm design.