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Data reuse driven energy-aware MPSoC co-synthesis of memory and communication architecture for streaming applications

机译:数据重用驱动的节能型MPSoC存储器和通信体系结构的综合用于流应用程序

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The memory subsystem of a complex multiprocessor systems-on-chip (MPSoC) is an important contributor to the chip power consumption. The selection of memory architecture, as well as of communication architecture, both affect the power efficiency of the design. In this paper we propose a novel approach that enables energy-aware co-synthesis of both memory and communication architecture for streaming applications. As opposed to earlier techniques, we employ a powerful compile-time analysis of memory access behavior that adds flexibility in selecting memory architectures. Additionally, we target TDMA bus-based communication architectures, which not only guarantee performance, but also greatly reduce the design time and allow us to find the energy optimal system configuration. We propose and compare three techniques: an optimal mixed ILP-based co-synthesis technique, a mixed ILP-based traditional two-step synthesis approach where memory and communication synthesis is performed sequentially, and a co-synthesis heuristic that synthesizes energy-efficient hierarchical bus-based communication architectures with guaranteed throughput. Our experimental results on a number of streaming applications show that both the traditional two-step synthesis approach and heuristic result in up to 50% worse power consumption in comparison with proposed co-synthesis approach. However, on some of the streaming benchmarks, our co-synthesis heuristic approach was able to find optimal or near-optimal results in a much shorter time than the MILP co-synthesis approach.
机译:复杂的多处理器片上系统(MPSoC)的内存子系统是芯片功耗的重要因素。存储器架构以及通信架构的选择均会影响设计的电源效率。在本文中,我们提出了一种新颖的方法,该方法可以针对流应用程序实现内存和通信体系结构的能量感知共合成。与较早的技术相反,我们对内存访问行为进行了强大的编译时分析,从而增加了选择内存体系结构的灵活性。此外,我们的目标是基于TDMA总线的通信体系结构,它不仅可以保证性能,而且可以大大减少设计时间,并使我们能够找到能量最佳的系统配置。我们提出并比较了三种技术:最优的基于混合ILP的共合成技术,基于混合ILP的传统两步合成方法(按顺序执行内存和通信合成)以及用于合成节能分层结构的共合成启发式算法保证吞吐量的基于总线的通信体系结构。我们在许多流媒体应用程序上的实验结果表明,与建议的共合成方法相比,传统的两步合成方法和启发式方法均导致功耗降低多达50%。但是,在某些流基准测试中,我们的协同合成启发式方法能够在比MILP协同合成方法短得多的时间内找到最佳或接近最佳的结果。

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