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首页> 外文期刊>Indian Journal of Science and Technology >Implementation of Multi-threading on Hybrid Arm Cortex Dual Core A9-fpga Architecture for Energy Efficiency
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Implementation of Multi-threading on Hybrid Arm Cortex Dual Core A9-fpga Architecture for Energy Efficiency

机译:在混合Arm Cortex双核A9-fpga架构上实现多线程以提高能源效率

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摘要

One of the greatest challenges of FPGA is the crave for a platform, which runs a system, handles a software and co-ordinates exchange with peripherals. To explore this advantage on FPGA with energy efficiency, this work aims at developing multithreading on ARM cortex dual core A9 processor present in Zedboard FPGA. To enhance the speed of execution on this processor and to allow concurrency, multithreading is developed using intel thread building blocks(TBB) library which aids in creating task based parallelism on FPGA being the first of its kind. In the first step towards this process, the processor is booted with Ubuntu 12.04 linux operating system making it as a standalone processor. This system is enabled with graphical user interface using xillinux IP library FPGA code kit. To test the efficiency of multihreading on the processing system of Zedboard, an application known as 2D - raytracer is developed using the parallel-for loop scheduling method which aids in running all the iteration loops in the code into chunks and runs each chunk as a separate thread under intel TBB. In this application an image was parallelized by speculating each pixel running in parallel resulting in excellent speedup. The newly implemented multithreading achieves a speedup of 53% compared to sequential execution on the same processor. This method explores the flexibility of parallel processing on FPGA.
机译:FPGA的最大挑战之一是对平台的渴望,该平台运行一个系统,处理一个软件并协调与外围设备的交换。为了探索具有能源效率的FPGA的这一优势,这项工作旨在开发Zedboard FPGA中存在的ARM cortex双核A9处理器上的多线程。为了提高在该处理器上的执行速度并允许并发,使用英特尔线程构建块(TBB)库开发了多线程,该库有助于在FPGA上创建基于任务的并行性。在此过程的第一步中,该处理器使用Ubuntu 12.04 linux操作系统启动,使其成为独立处理器。该系统使用xillinux IP库FPGA代码套件通过图形用户界面启用。为了在Zedboard的处理系统上测试多重读取的效率,使用并行循环调度方法开发了一个称为2D-raytracer的应用程序,该方法有助于将代码中的所有迭代循环运行到块中,并将每个块作为单独的块运行线程在英特尔TBB下。在该应用中,通过推测并行运行的每个像素对图像进行了并行处理,从而实现了出色的加速效果。与在同一处理器上顺序执行相比,新实现的多线程实现了53%的加速。这种方法探索了FPGA上并行处理的灵活性。

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