适应CNFET中的进程变化以实现高效的末级Cache设计
Taming Process Variations in CNFET for Efficient Last Level Cache Design
摘要：
碳纳米管场效应晶体管（CNFET）因其更高的速度和能效而成为CMOS晶体管的一种有前途的替代品，这使得该技术特别适合于构建耗能的末级缓存（LLC）。然而，在CNFET的工艺变化（PV）所造成的不完美的制造导致大的时序变化和最坏情况下的时序显着限制LLC的操作速度。特别是，我们观察到基于CNFET的缓存延迟分布与LLC布局密切相关。针对CNT生长方向分别与缓存路径方向和缓存组方向对齐的两种典型LLC布局，我们提出了可变感知组对齐（VASA）缓存和可变感知路径对齐（VAWA）缓存，并结合相应的缓存优化（如数据重排和页面映射），实现了对常用数据的低延迟缓存。实验结果表明，在两种不同的CNFET版图上，优化后的LLC与基线设计相比，平均访问延迟分别降低了32%和45%，整体性能分别提高了6%和9%，能耗分别降低了4%和8%.此外，在统一模型中考虑了架构引起的延迟变化和PV引起的延迟变化，我们扩展了基于CNFET的NUCA的VAWA和VASA缓存设计，与简单的变化感知NUCA相比，所提出的NUCA实现了显着的性能改进和节能。
英文摘要：
Carbon nanotube field-effect transistors (CNFET) emerge as a promising alternative to CMOS transistors for the much higher speed and energy efficiency, which makes the technology particularly suitable for building the energy-hungry last level cache (LLC). However, the process variations (PVs) in CNFET caused by the imperfect fabrication lead to large timing variation and the worst-case timing dramatically limits the LLC operation speed. Particularly, we observe that the CNFET-based cache latency distribution is closely related to the LLC layouts. For the two typical LLC layouts that have the CNT growth direction aligned to the cache way direction and cache set direction respectively, we proposed variation-aware set aligned (VASA) cache and variation-aware way aligned (VAWA) cache in combination with corresponding cache optimizations such as data shuffling and page mapping to enable low-latency cache for frequently used data. According to our experiments, the optimized LLC reduces the average access latency by 32% and 45% compared to the baseline designs on the two different CNFET layouts respectively while it improves the overall performance by 6\% and 9\% and reduces the energy consumption by 4% and 8% respectively. In addition, with both the architecture induced latency variation and PV incurred latency variation considered in a unified model, we extended the VAWA and VASA cache design for the CNFET-based NUCA and the proposed NUCA achieves both significant performance improvement and energy saving compared to the straightforward variation-aware NUCA.
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