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Minimal Placement of Bank Selection Instructions for Partitioned Memory Architectures

机译:分区存储架构的存储体选择指令的最小放置

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We have devised an algorithm for minimal placement of bank selections in partitioned memory architectures. This algorithm is parameterizable for a chosen metric, such as speed, space, or energy. Bank switching is a technique that increases the code and data memory in microcontrollers without extending the address buses. Given a program in which variables have been assigned to data banks, we present a novel optimization technique that minimizes the overhead of bank switching through cost-effective placement of bank selection instructions. The placement is controlled by a number of different objectives, such as runtime, low power, small code size or a combination of these parameters. We have formulated the minimal placement of bank selection instructions as a discrete optimization problem that is mapped to a partitioned Boolean quadratic programming (PBQP) problem. We implemented the optimization as part of a PIC Microchip backend and evaluated the approach for several optimization objectives. Our benchmark suite comprises programs from MiBench and DSPStone plus a microcontroller real-time kernel and drivers for microcontroller hardware devices. Our optimization achieved a reduction in program memory space of between 2.7 and 18.2%, and an overall improvement with respect to instruction cycles between 5.0 and 28.8%. Our optimization achieved the minimal solution for all benchmark programs. We investigated the scalability of our approach toward the requirements of future generations of microcontrollers. This study was conducted as a worst-case analysis on the entire MiBench suite. Our results show that
机译:我们设计了一种算法,以在分区内存体系结构中最小化存储体选择。该算法可针对所选指标(例如速度,空间或能量)进行参数设置。存储体切换是一种在不扩展地址总线的情况下增加微控制器中的代码和数据存储器的技术。给定一个已将变量分配给数据库的程序,我们提出一种新颖的优化技术,该方法可通过经济高效地放置存储区选择指令来最大程度地减少存储区切换的开销。放置由许多不同的目标控制,例如运行时间,低功耗,较小的代码大小或这些参数的组合。我们已经将存储体选择指令的最小放置公式化为离散优化问题,并将其映射到分区布尔二次规划(PBQP)问题。我们将优化作为PIC Microchip后端的一部分进行了实施,并针对几种优化目标评估了该方法。我们的基准套件包括来自MiBench和DSPStone的程序,以及微控制器实时内核和微控制器硬件设备的驱动程序。我们的优化使程序存储器空间减少了2.7%至18.2%,并且相对于指令周期而言,总体上提高了5.0%至28.8%。我们的优化为所有基准程序实现了最小的解决方案。我们研究了我们方法的可扩展性,以适应下一代微控制器的需求。这项研究是对整个MiBench套件进行的最坏情况分析。我们的结果表明

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