MOOS
MOOS
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莫斯
DOI：
10.1145/3358206
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
P. Pande
中科院分区：
文献类型：
--
作者：
Aryan Deshwal;Nitthilan Kanappan Jayakodi;B. K. Joardar;J. Doppa;P. Pande
不断增长的需求，新兴的应用程序提出了重大的挑战，优化众核系统的设计。片上网络（NoC）使得能够在单个管芯中集成大量处理元件（PE）。为了设计优化的众核系统，我们需要在包括功耗、性能和散热在内的多个目标之间建立适当的权衡。因此，我们考虑在NoC使能的众核系统的设计中出现的多目标设计空间探索（MO-DSE）问题：PE和通信链路的放置以优化两个或更多个目标（例如，等待时间、能量和吞吐量）。现有的算法来解决MO-DSE问题遭受的可扩展性和准确性的挑战，作为设计空间的大小和目标的数量的增长。在本文中，我们提出了一种新的框架，称为多目标乐观搜索（MOOS），使用数据驱动的模型进行自适应设计空间探索，以提高多目标设计优化过程的速度和准确性。我们应用MOOS设计三维异构和同质众核系统使用Rodinia，PARSEC，和SPLASH2基准套件。我们证明，与最先进的方法相比，MOOS将寻找解决方案的速度提高了13倍，同时发现了在NoC方面更好的设计高达20%。与针对PE放置进行优化的基于3D网格的设计相比，优化的3D众核系统将EDP提高了38%。
The growing needs of emerging applications has posed significant challenges for the design of optimized manycore systems. Network-on-Chip (NoC) enables the integration of a large number of processing elements (PEs) in a single die. To design optimized manycore systems, we need to establish suitable trade-offs among multiple objectives including power, performance, and thermal. Therefore, we consider multi-objective design space exploration (MO-DSE) problems arising in the design of NoC-enabled manycore systems: placement of PEs and communication links to optimize two or more objectives (e.g., latency, energy, and throughput). Existing algorithms to solve MO-DSE problems suffer from scalability and accuracy challenges as size of the design space and the number of objectives grow. In this paper, we propose a novel framework referred as Multi-Objective Optimistic Search (MOOS) that performs adaptive design space exploration using a data-driven model to improve the speed and accuracy of multi-objective design optimization process. We apply MOOS to design both 3D heterogeneous and homogeneous manycore systems using Rodinia, PARSEC, and SPLASH2 benchmark suites. We demonstrate that MOOS improves the speed of finding solutions compared to state-of-the-art methods by up to 13X while uncovering designs that are up to 20% better in terms of NoC. The optimized 3D manycore systems improve the EDP up to 38% when compared to 3D mesh-based designs optimized for the placement of PEs.