SoftWear: Software-Only In-Memory Wear-Leveling for Non-Volatile Main Memory
Christian Hakert, Kuan-Hsun Chen, Paul R. Genssler, Georg von der, Br\"uggen, Lars Bauer, Hussam Amrouch, Jian-Jia Chen, J\"org Henkel

TL;DR
This paper introduces a software-only approach to in-memory wear-leveling for non-volatile memory, leveraging CPU features to extend memory lifetime without additional hardware support.
Contribution
It proposes a novel software-based wear-leveling method using CPU features like MMU and performance counters, eliminating the need for extra hardware.
Findings
Achieves up to 78.43% of ideal memory lifetime.
Extends memory lifetime by over a factor of 900.
Provides page-level and fine-grained wear-leveling techniques.
Abstract
Several emerging technologies for byte-addressable non-volatile memory (NVM) have been considered to replace DRAM as the main memory in computer systems during the last years. The disadvantage of a lower write endurance, compared to DRAM, of NVM technologies like Phase-Change Memory (PCM) or Ferroelectric RAM (FeRAM) has been addressed in the literature. As a solution, in-memory wear-leveling techniques have been proposed, which aim to balance the wear-level over all memory cells to achieve an increased memory lifetime. Generally, to apply such advanced aging-aware wear-leveling techniques proposed in the literature, additional special hardware is introduced into the memory system to provide the necessary information about the cell age and thus enable aging-aware wear-leveling decisions. This paper proposes software-only aging-aware wear-leveling based on common CPU features and does…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Advanced Memory and Neural Computing · Interconnection Networks and Systems
