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Rapid Energy Estimation for Hardware-Software Codesign Using FPGAs

机译:使用FPGA的硬件-软件协同设计的快速能量估算

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By allowing parts of the applications to be executed either on soft processors (as software programs) or on customized hardware peripherals attached to the processors, FPGAs have made traditional energy estimation techniques inefficient for evaluating various design tradeoffs. In this paper, we propose a high-level simulation-based two-step rapid energy estimation technique for hardware-software codesign using FPGAs. In the first step, a high-level hardware-software cosimulation technique is applied to simulate both the hardware and software components of the target application. High-level simulation results of both software programs running on the processors and the customized hardware peripherals are gathered during the cosimulation process. In the second step, the high-level simulation results of the customized hardware peripherals are used to estimate the switching activities of their corresponding register-transfer/gate level ("low-level") implementations. We use this information to employ an instruction-level energy estimation technique and a domain-specific energy performance modeling technique to estimate the energy dissipation of the complete application. A Matlab/Simulink-based implementation of our approach and two numerical computation applications show that the proposed energy estimation technique can achieve more than 6000x speedup over low-level simulation-based techniques while sacrificing less than 10% estimation accuracy. Compared with the measured results, our experimental results show that the proposed technique achieves an average estimation error of less than 12%.
机译:通过允许部分应用程序在软处理器(作为软件程序)或在连接到处理器的定制硬件外围设备上执行,FPGA使传统的能量估算技术无法有效地评估各种设计折衷。在本文中,我们提出了一种基于高级仿真的两步快速能量估算技术,用于使用FPGA的软硬件代码签名。第一步,应用高级软硬件协同仿真技术来模拟目标应用程序的硬件和软件组件。在协同仿真过程中,将收集运行在处理器和定制硬件外围设备上的两个软件程序的高级仿真结果。在第二步中,使用定制的硬件外设的高级仿真结果来估计其相应的寄存器传输/门级(“低级”)实现的开关活动。我们使用此信息来采用指令级能量估计技术和特定领域的能量性能建模技术来估计整个应用程序的能量消耗。我们的方法基于Matlab / Simulink的实现以及两个数值计算应用表明,与基于低级仿真的技术相比,所提出的能量估算技术可实现6000倍以上的加速,同时牺牲不到10%的估算精度。与测量结果相比,我们的实验结果表明,所提出的技术实现了小于12%的平均估计误差。

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