Improving write operations in MLC phase change memory

TLDR

Phase‑change memory, especially multi‑level cell PCM, offers high density and low cost but suffers from very slow write operations that hinder its use in modern memory hierarchies. The authors propose architectural innovations—write truncation and form switch—to reduce write latency and ECC overhead in MLC PCM. Write truncation limits the number of iterative writes using an auxiliary error‑correcting code, while form switch stores highly compressible lines in single‑level‑cell form to cut ECC overhead and improve read latency. Experiments show that write truncation and form switch reduce effective write and read latency by 57 % and 28 % respectively, yielding a 26 % overall performance gain over the state of the art.

Abstract

Phase change memory (PCM) recently has emerged as a promising technology to meet the fast growing demand for large capacity memory in modern computer systems. In particular, multi-level cell (MLC) PCM that stores multiple bits in a single cell, offers high density with low per-byte fabrication cost. However, despite many advantages, such as good scalability and low leakage, PCM suffers from exceptionally slow write operations, which makes it challenging to be integrated in the memory hierarchy. In this paper, we propose architectural innovations to improve the access time of MLC PCM. Due to cell process variation, composition fluctuation and the relatively small differences among resistance levels, MLC PCM typically employs an iterative write scheme to achieve precise control, which suffers from large write access latency. To address this issue, we propose write truncation (WT) to reduce the number of write iterations with the assistance of an extra error correction code (ECC). We also propose form switch (FS) to reduce the storage overhead of the ECC. By storing highly compressible lines in SLC form, FS improves read latency as well. Our experimental results show that WT and FS improve the effective write/read latency by 57%/28% respectively, and achieve 26% performance improvement over the state of the art.