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Low‐power heterogeneous uncore architecture for future 3D chip‐multiprocessors

机译:适用于未来3D芯片多处理器的低功耗异构非核心架构

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Uncore components such as on‐chip memory systems and on‐chip interconnects consume a large amount of energy in emerging embedded applications. Few studies have focused on next‐generation analytical models for future chip‐multiprocessors ( CMP s) that simultaneously consider the impacts of the power consumption of core and uncore components. In this paper, we propose a convex‐optimization approach to design heterogeneous uncore architectures for embedded CMP s. Our convex approach optimizes the number and placement of memory banks with different technologies on the memory layer. In parallel with hybrid memory architecting, optimizing the number and placement of through silicon vias as a viable solution in building three‐dimensional (3D) CMP s is another important target of the proposed approach. Experimental results show that the proposed method outperforms 3D CMP designs with hybrid and traditional memory architectures in terms of both energy delay products ( EDP s) and performance parameters. The proposed method improves the EDP s by an average of about 43% compared with SRAM design. In addition, it improves the throughput by about 7% compared with dynamic RAM ( DRAM ) design.
机译:诸如片上存储系统和片上互连之类的非核心组件在新兴的嵌入式应用中会消耗大量能量。很少有研究集中在针对未来芯片多处理器(CMP)的下一代分析模型上,该模型同时考虑了核心和非核心组件功耗的影响。在本文中,我们提出了一种凸优化方法来设计嵌入式CMP的异构非核体系结构。我们的凸面方法可以优化在存储层上使用不同技术的存储体的数量和位置。与混合内存架构设计同时进行,优化硅通孔的数量和位置作为构建三维(3D)CMP的可行解决方案是该方法的另一个重要目标。实验结果表明,该方法在能量延迟乘积(EDP)和性能参数方面均优于混合和传统内存架构的3D CMP设计。与SRAM设计相比,所提出的方法将EDP平均提高了约43%。此外,与动态RAM(DRAM)设计相比,它将吞吐量提高了约7%。

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