Read/Write Disturbance-Aware Design for MLC STT-RAM-based Cache

Yao-Hung Huang, Jen-Wei Hsieh
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Abstract

Spin-transfer torque RAM (STT-RAM) has been considered as a promising candidate for the next generation on-chip last-level cache (LLC) due to its high cell density, non-volatility, and near-zero standby power. To further improve cell density, multi-level cell (MLC) STT-RAM has been proposed and widely adopted. However, applying MLC STT-RAM to LLC might suffer from both write disturbance (WD) and read disturbance (RD). WD that needs two-step write operations to write data in MLC STT-RAM cell incurs extra energy consumption and latency overhead. RD means that reading data from a cell will also disturb the original data. In this paper, we propose a read/write disturbance-aware (RWDA) design for MLC STT-RAM-based cache to reduce the overhead caused by the WD and RD. We delay restore operations to mitigate the adverse impacts of disturbances. Instead of the typical LRU replacement policy, we propose a priority-based victim selection policy to meet the very distinct characteristics of MLC STT-RAM. Since accessing soft bits is much more beneficial than accessing hard bits in terms of access latency and energy consumption, we adopt a swapping mechanism to exchange frequently accessed data from hard bits to soft bits. The experimental results showed that the proposed design could averagely achieve 26.6% energy-consumption reduction and 29.5% IPC of system-performance improvement, compared with the conventional design of MLC STT-RAM cache.
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基于MLC stt - ram的缓存读写干扰感知设计
自旋转移扭矩RAM (STT-RAM)由于其高单元密度、非易失性和接近零待机功率,被认为是下一代片上最后一级缓存(LLC)的有希望的候选者。为了进一步提高小区密度,多层小区(MLC) STT-RAM被提出并广泛采用。然而,将MLC STT-RAM应用于LLC可能同时存在写干扰(WD)和读干扰(RD)。需要两步写操作才能在MLC STT-RAM单元中写入数据的WD会产生额外的能耗和延迟开销。RD意味着从单元格中读取数据也会干扰原始数据。在本文中,我们提出了一种基于MLC stt - ram的读/写干扰感知(RWDA)设计,以减少WD和RD造成的开销。我们延迟恢复操作以减轻干扰的不利影响。我们提出了一种基于优先级的受害者选择策略来代替典型的LRU替换策略,以满足MLC STT-RAM非常明显的特点。由于访问软位在访问延迟和能量消耗方面比访问硬位更有利,因此我们采用交换机制将频繁访问的数据从硬位交换到软位。实验结果表明,与传统的MLC STT-RAM缓存设计相比,该设计平均可实现26.6%的能耗降低和29.5%的系统性能提升。
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CiteScore
1.70
自引率
14.30%
发文量
17
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