Run‐time neuro‐fuzzy type‐2 controller for power optimisation of GP‐GPU architecture

Abstract

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.