Storage coding for wear leveling in flash memories

Authors:
Anxiao Jiang;Robert Mateescu;Eitan Yaakobi;Jehoshua Bruck;Paul H. Siegel;Alexander Vardy;Jack K. Wolf
Affiliations:
Department of Computer Science and Engineering, Texas A&M University, College Station, TX;Microsoft Research Cambridge, Cambridge, UK;Department of Electrical and Computer Engineering and the Center for Magnetic Recording Research, University of California at San Diego, La Jolla, CA;Department of Electrical Engineering, California Institute of Technology, Pasadena, CA;Department of Electrical and Computer Engineering and the Center for Magnetic Recording Research, University of California at San Diego, La Jolla, CA;Department of Electrical and Computer Engineering, Department of Computer Science and Engineering, and the Department of Mathematics, University of California at San Diego, La Jolla, CA;Department of Electrical and Computer Engineering and the Center for Magnetic Recording Research, University of California at San Diego, La Jolla, CA
Venue:
IEEE Transactions on Information Theory
Year:
2010

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Abstract

Flash memory is a nonvolatile computer memory comprised of blocks of cells, wherein each cell is implemented as either NAND or NOR floating gate. NAND flash is currently the most widely used type of flash memory. In a NAND flash memory, every block of cells consists of numerous pages; rewriting even a single page requires the whole block to be erased and reprogrammed. Block erasures determine both the longevity and the efficiency of a flash memory. Therefore, when data in a NAND flash memory are reorganized, minimizing the total number of block erasures required to achieve the desired data movement is an important goal. This leads to the flash data movement problem studied in this paper. We show that coding can significantly reduce the number of block erasures required for data movement, and present several optimal or nearly optimal data-movement algorithms based upon ideas from coding theory and combinatorics. In particular, we show that the sorting-based (noncoding) schemes require O(n log n) erasures to move data among n blocks, whereas coding-based schemes require only O(n) erasures. Furthermore, coding-based schemes use only one auxiliary block, which is the best possible and achieve a good balance between the number of erasures in each of the n + 1 blocks.