Low Overhead Software Wear Leveling for Hybrid PCM + DRAM Main Memory on Embedded Systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

24 Scopus Citations
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Author(s)

  • Jingtong Hu
  • Mimi Xie
  • Chen Pan
  • Qingfeng Zhuge
  • Edwin H.-M. Sha

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number6820777
Pages (from-to)654-663
Journal / PublicationIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume23
Issue number4
Online published23 May 2014
Publication statusPublished - Apr 2015

Abstract

Phase change memory (PCM) is a promising DRAM replacement in embedded systems due to its attractive characteristics, such as low-cost, shock-resistivity, nonvolatility, high density, and low leakage power. However, relatively low endurance has limited its practical applications. In this paper, in addition to existing hardware level optimizations, we propose software enabled wear-leveling techniques to further extend PCMs lifetime when it is adopted in embedded systems. Most existing software optimization techniques focus on reducing the total number of writes to PCM, but none of them consider wear leveling, in which the writes are distributed more evenly over the PCM. An integer linear programming formulation and a polynomial-time algorithm, the software wear-leveling algorithm, are proposed in this paper to achieve wear leveling without hardware overhead. According to the experimental results, the proposed techniques can reduce the number of writes on the most-written addresses by more than 80% when compared with a greedy algorithm, and by more than 60% when compared with the existing optimal data allocation algorithm with under 6% memory access overhead.

Research Area(s)

  • DRAM, energy, main memory, nonvolatile memories (NVMs), phase change memory (PCM), wear leveling, write reduction

Citation Format(s)

Low Overhead Software Wear Leveling for Hybrid PCM + DRAM Main Memory on Embedded Systems. / Hu, Jingtong; Xie, Mimi; Pan, Chen; Xue, Chun Jason; Zhuge, Qingfeng; Sha, Edwin H.-M.

In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 23, No. 4, 6820777, 04.2015, p. 654-663.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review