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Accelerating a Classic 3D Video Game on Heterogeneous Reconfigurable MPSoCs

机译:加速在异构可重构的MPsocs上的经典3D视频游戏

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Heterogeneous Reconfigurable MPSoCs, coupling microprocessors with Programmable Logic, are becoming extremely important in High-Performance Embedded Computing domain where energy consumption is a key factor to be considered by every designer. However, efficient hardware/software co-design still requires experience and a big effort: finding an optimal solution and an acceptable trade-off between performance and energy may require several tests and it is strongly platform-dependent. To this respect, a Dataflow-based method is used in this work for exploring different hardware/software configurations (number of hardware accelerators and FPGA frequency). As a use case, the acceleration of a well-known 3D video game (DOOM) is presented. The method offers rapid trade-off analysis in terms of non-functional parameters such as computing performance or power/energy measurements. Extensive experimental results show that is possible to speed up the game and, at the same time, reduce the energy consumption of the whole platform. A custom Linux-based Operating System for Zynq Ultrascale+ was created, including a GPU driver to support a graphical interface on an HDMI screen and drivers to manage custom hardware accelerators on the FPGA side. The best solution to save up to 63% of energy corresponds to the use of four parallel hardware accelerators, where a function speed up of x3.6 and an application speed up of x2 (in line with Amdahl's law) is obtained. Additionally, a set of Pareto optimal solutions are reported in the results section.
机译:具有可编程逻辑的微处理器的异构可重构的MPSOCs在高性能嵌入式计算领域中变得非常重要,其中能量消耗是每个设计者考虑的关键因素。然而,高效的硬件/软件共同设计仍然需要经验和大量努力:在性能和能量之间找到最佳解决方案和可接受的权衡可能需要多个测试,并且它具有强烈的平台依赖性。为此,在这项工作中使用基于数据流的方法来探索不同的硬件/软件配置(硬件加速器数量和FPGA频率)。作为用例,呈现了众所周知的3D视频游戏(DOOM)的加速度。该方法在非功能性参数方面提供了快速的折衷分析,例如计算性能或功率/能量测量。广泛的实验结果表明,可以加快游戏,同时可以降低整个平台的能量消耗。创建了一种用于Zynq UltraScale +的自定义Linux的操作系统,包括GPU驱动程序,以支持HDMI屏幕和驱动程序上的图形界面,以管理FPGA侧的自定义硬件加速器。节省高达63%能量的最佳解决方案对应于使用四个并联硬件加速器,其中X3.6的功能加速和X2的应用速度(符合Amdahl的定律)。此外,结果部分报告了一组Pareto最佳解决方案。

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