אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Archival data once written are rarely accessed by user, and need to be reliably retained for long periods of time. The challenge of using inexpensive NAND flash to archive cold data was posed recently for saving data center costs. Solid state drives are faster, more power-efficient and mechanically reliable than hard drives (HDs). However, flash of high density is vulnerable to charge leakage over time, and can only be cost-competitive to HD in archival systems if longer retention periods (RPs) are achieved. Moreover, the size of archival data grows exponentially each year, which makes finding the data we need more difficult.

This talk describes two examples of our on-going research to address the issues above. We first present the implementation of a coding technique named rank modulation (RM). RM reads data using the relative order of cell voltages, and is more resilient to retention errors. We show that combining RM and memory scrubbing provides more than 100 years of retention period for 1x-nm triple-level cell NAND flash. We then demonstrate an associative memory framework. The framework utilizes the random-access capability of flash memory, and solves word association puzzles with good precision and fast speed using crowd-sourced data. We show the similarities between puzzle solving and data retrieval, and discuss our plans on expanding the current framework for more realistic data retrieval applications.

Bio:
Yue is a postdoctoral fellow at California Institute of Technology. His research focuses on algorithms and data representations for emerging non-volatile memories. Yue worked as a research intern at LSI in 2013. He received Ph. D. in computer science from Texas A&M University in 2014.