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Design-Time Performance Evaluation of Thermal Management Policies for SRAM and RRAM based 3D MPSoCs

机译:SRAM和RRAM基于3D MPsocs热管理策略的设计时间绩效评估

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3D-ICs hold significant promise for future generation imilti processor syst.ems-on-ehip due to their potential for increased performance, decreased power, heterogeneous integration, and reduced cost over planar ICs. However, the vertical integration of these structures exacerbates the heat dissipation and run-time thermal management issues. There have been a number of design- and run-time thermal management policies proposed, but few focus on examining overall system performance. Additionally, the heterogeneity of 3D-ICs allows for the integration of novel technologies, such as resistive: random access memories (RRAMs). which offer higher density and lower power than traditional CMOS memory technologies. Our work presents a flexible design-time simulation framework to evaluate system performance and thermal profiles of 3D MPSoCs. We utilize this framework to study the effect of three dynamic thermal management policies (air-cooled load balancing, liquid-cooled load balancing, and air-cooled DYFS) on system performance and die temperature for multi-tiered 3D MPSoCs utilizing SRAM and RRAM-based 1,2 caches. We find that RRAM-based caches lower overall average maximum temperatures by 120 K and 24 K for air and liquid cooling systems, respectively (when compared to SRAM-based caches), at a worst-case performance delay of 47% and best-case delay of 13% for the parallel shared-niemorv benchmarks studied.
机译:3D-ICS由于其潜力而增加了性能,功率,异构集成和降低了平面IC的潜力,因此对未来一代IMILTI处理器SYST.EMS-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON-ON。然而,这些结构的垂直整合加剧了散热和运行时间热管理问题。提出了许多设计和运行时间热管理策略,但很少专注于检查整体系统性能。另外,3D-IC的异质性允许集成新技术,例如电阻:随机访问存储器(RRAM)。它提供比传统CMOS内存技术更高的密度和低功率。我们的工作介绍了一个灵活的设计时仿真框架,可以评估3D MPSoC的系统性能和热谱。我们利用本框架来研究三种动态热管理政策(风冷负载平衡,液冷载负载和空气冷却DYF)对利用SRAM和RRAM的多层3D MPSoC的系统性能和模具温度的影响基于1,2缓存。我们发现,对于空气和液体冷却系统(与SRAM的高速缓存相比,RRAM为基于CACH的总平均最大温度降低了120 k和24K,以47%的最坏情况延迟和最佳情况对所学习的并行共享-Niemorv基准延迟13%。

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