In this paper, we are interested in the energy optimization of hybrid Scratchpad Memory (SPM) which consists of SRAM and Non-Volatile Memory (NVM). We approach the energy optimization by data allocation with the consideration of the impact of program structure like branches. Our method is composed of two stages, i.e., program analysis stage and data allocation stage. In program analysis stage, we design two strategies to predict the branch execution of the program and reduce the number of reading/writing operations. Specifically, we propose Branch-based Static Analysis (BSA) strategy for simple programs and Neural Network Branch Prediction (NNBP) strategy for complex programs separately. In data allocation stage, we propose an Energy-based Data Allocation (EDA) strategy to reduce the energy consumption, which can determine how to allocate/migrate data variables onto SRAM/NVM. Incorporating these two stages, we have two branch-aware data variable allocation approaches, i.e., BSA+EDA for simple programs and NNBP+EDA for complex programs. Based on the existing benchmarks, we conduct extensive experiments to evaluate the proposed approaches. The experimental results show that both BSA+EDA and NNBP+EDA can effectively reduce the energy consumption up to 39.4% compared with the other approaches. Moreover, experiments demonstrate that BSA+EDA is suitable for simple programs whereas NNBP+EDA is suitable for complex programs.

在本文中，我们对由SRAM和非易失性存储器（NVM）组成的混合Scratchpad存储器（SPM）的能量优化感兴趣。我们通过考虑诸如分支等程序结构的影响，通过数据分配来实现能源优化。我们的方法包括两个阶段，即程序分析阶段和数据分配阶段。在程序分析阶段，我们设计了两种策略来预测程序的分支执行并减少读取/写入操作的数量。具体来说，我们分别针对简单程序提出了基于分支的静态分析（BSA）策略，为复杂程序提出了基于神经网络的分支预测（NNBP）策略。在数据分配阶段，我们提出了一种基于能源的数据分配（EDA）策略，以减少能耗，可以确定如何将数据变量分配/迁移到SRAM / NVM。结合这两个阶段，我们有两种分支感知数据变量分配方法，即对于简单程序为BSA + EDA，对于复杂程序为NNBP + EDA。在现有基准的基础上，我们进行了广泛的实验，以评估所提出的方法。实验结果表明，与其他方法相比，BSA + EDA和NNBP + EDA均可有效降低能耗达39.4％。此外，实验证明BSA + EDA适用于简单程序，而NNBP + EDA适用于复杂程序。我们进行了广泛的实验，以评估建议的方法。实验结果表明，与其他方法相比，BSA + EDA和NNBP + EDA均可有效降低能耗达39.4％。此外，实验证明BSA + EDA适用于简单程序，而NNBP + EDA适用于复杂程序。我们进行了广泛的实验，以评估建议的方法。实验结果表明，与其他方法相比，BSA + EDA和NNBP + EDA均可有效降低能耗达39.4％。此外，实验证明BSA + EDA适用于简单程序，而NNBP + EDA适用于复杂程序。