Signal Processing and Coding for Non-Volatile Memories




Abstract: Coding and signal processing for nonvolatile memories will be an important development in data storage technologies. Currently, there has been lots of interest in coding for Flash Memories, and many results have been derived. Like magnetic recording and optical recording, Flash Memories have their own distinct properties, including recursive programming, block erasure, etc. These distinct properties introduce very interesting coding problems that address many aspects of a successful storage system, which include efficient data modification, error correction, high density storage, etc. In this talk, we will review the current NAND Flash Memory technology, including its real-life noise sources and how they influence the design of the memories. We will then discuss new topics in coding theory for data storage in Flash Memories, including codes for rewriting data, error-correcting codes, rank modulation, variable-level cell, and more.

Bio: Jason Bellorado graduated Summa Cum Laude from the University of Massachusetts with a BSEE in 2000, and received a Ph.D. in Engineering Sciences from Harvard University in 2006. In 2006 Jason joined Link_A_Media Devices as a system architect and was promoted to the position of Director of Engineering in 2011. Jason now oversees all system architecture activities for Link_A_Media Devices and, since its acquisition by SKHynix in 2012, SK Hynix Memory Solutions.

Anxiao (Andrew) Jiang is an associate professor in the Computer Science and Engineering Department of Texas A&M University. He received his Ph.D. and M.S. in Electrical Engineering at Caltech and his B.E. in Electronic Engineering at Tsinghua University. He received the National Science Foundation CAREER Award for his exploratory work on coding for flash memories. He is 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.

Eitan Yaakobi is a postdoctoral researcher in the department of Electrical Engineering at the California Institute of Technology, where he works with Prof. Shuki Bruck. He is also affiliated with the Center for Magnetic Recording Research at the University of California, San Diego. He received a PhD. degree in Electrical and Computer Engineering at the University of California, San Diego, under the supervision of Prof. Paul Siegel, Prof. Alexander Vardy, and Prof. Jack Wolf. He received the Marconi Society Young Scholar Award in 2009 and the Intel Ph.D. Fellowship in 2010-2011. His research interests include information and coding theory with applications to non-volatile memories, associative memories, data storage and retrieval, and voting theory.