
Emerging Non-Volatile Memories (NVMs) such as Phase Change Memory (PCM) and Resistive RAM (RRAM) are promising to replace traditional DRAM technology. However, they suffer from limited write endurance and high write energy consumption. Encoding methods such as Flip-N-Write, FlipMin and CAFO can reduce the bit flips of NVMs by exploiting additional capacity to store the tag bits of encoding methods. The effects of encoding methods are limited by the capacity overhead of the tag bits. In this paper, we propose COE to COmpress cacheline for Extending the lifetime of NVMs. COE exploits the space saved by compression to store the tag bits of data encoding methods. Through combining data compression techniques with data encoding methods, COE can reduce the bit flips with negligible capacity overhead. We further observe that the saved space size of each compressed cacheline varies, and different encoding methods have different tradeoffs between capacity overhead and effects. To fully exploit the space saved by compression for improving lifetime, we select the proper encoding methods according to the saved space size. Experimental results show that our scheme can reduce the bit flips by 14.2%, decrease the energy consumption by 11.8% and improve the lifetime by 27.5% with only 0.2% capacity overhead.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
