The increasing demand for high-performance computing has emphasised the invocation of sophisticated multi/many-core computing architecture. Graphical Processing Unit (GPU) is considered to be an essential innovation in this regard as GPU offers a significant amount of parallelism in the execution of complex computing applications. The performance of GPUs in reducing the computational time of such applications is worth mentioning. Although GPUs appear to be a problem-solving solution for complex applications yet high power consumption has been a challenging problem, associated with this many-core computer architecture. Efficient resource management is a emerging and promising solution to this challenge; however, reducing the resources would degrade the system's overall performance. On the other hand, reducing the resources based on the analysis of workload can save significant power without degrading the system's overall performance. Therefore, a smart controller to optimise the resources of general purpose-GPU (GP-GPU) architecture is required. AFBRMC-2, a neuro-fuzzy type-2 based controller, is presented for GP-GPU architecture and, based on a feedback mechanism, keeps analysing the stats of processor and manages resources using dynamic voltage frequency scaling and core gating techniques. The proposed controller achieved up to 55% reduction in power consumption against various benchmarks on the NVIDIA TK1 GPU kit.

中文翻译：

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用于GP-GPU架构功耗优化的运行时神经模糊2型控制器

对高性能计算的日益增长的需求强调了对复杂的多核/多核计算体系结构的调用。在这方面，图形处理单元（GPU）被认为是一项重要的创新，因为GPU在复杂计算应用程序的执行中提供了大量并行性。值得一提的是GPU在减少此类应用程序的计算时间方面的性能。尽管GPU似乎是解决复杂应用程序的问题的解决方案，但与这种多核计算机体系结构相关的高功耗却是一个具有挑战性的问题。高效的资源管理是应对这一挑战的新兴且有希望的解决方案；但是，减少资源会降低系统的整体性能。另一方面，根据工作负载分析减少资源可以节省大量电能，而不会降低系统的整体性能。因此，需要一种用于优化通用GPU（GP-GPU）架构资源的智能控制器。AFBRMC-2是一种基于神经模糊2类的控制器，针对GP-GPU体系结构而提出，并基于反馈机制，使用动态电压频率缩放和核心门控技术不断分析处理器的状态并管理资源。与NVIDIA TK1 GPU套件上的各种基准相比，建议的控制器可将功耗降低多达55％。提出了一种基于神经模糊2型控制器，用于GP-GPU架构，并基于反馈机制，使用动态电压频率缩放和核心门控技术不断分析处理器的状态并管理资源。与NVIDIA TK1 GPU套件上的各种基准相比，建议的控制器可将功耗降低多达55％。提出了一种基于神经模糊2型控制器，用于GP-GPU架构，并基于反馈机制，使用动态电压频率缩放和核心门控技术不断分析处理器的状态并管理资源。与NVIDIA TK1 GPU套件上的各种基准相比，建议的控制器可将功耗降低多达55％。