基于语法进化的嵌入式系统L1缓存优化
Optimizing L1 cache for embedded systems through grammatical evolution
摘要：
如今，嵌入式系统配备有足够大的高速缓存存储器，以影响性能和能耗，这在这种系统中从未发生过。此外，高速缓冲存储器系统已被识别为通过根据正在运行的应用程序的存储器访问模式调整其配置来改进那些度量的组件。然而，考虑到高速缓存存储器具有许多可以被设置为大量不同值的参数，设计者面临着广泛且耗时的探索空间。在本文中，我们提出了一个基于语法进化（GE）的优化框架，它能够有效地找到一组给定的基准测试应用程序的最佳缓存配置。这种元启发式算法允许优化运行时间的重要减少，在低数量的代中获得良好的结果。此外，这种减少也增加了由于评估缓存的有效存储。此外，我们选择GE是因为语法的可塑性简化了表型的创建，这些表型形成了对不同配置评估所需的缓存模拟器的调用。Mediabench套件的实验结果表明，我们的建议是能够找到缓存配置，获得平均改善$62\%$与现实世界的基线配置。
英文摘要：
Nowadays, embedded systems are provided with cache memories that are large enough to influence in both performance and energy consumption as never occurred before in this kind of systems. In addition, the cache memory system has been identified as a component that improves those metrics by adapting its configuration according to the memory access patterns of the applications being run. However, given that cache memories have many parameters which may be set to a high number of different values, designers face to a wide and time-consuming exploration space. In this paper we propose an optimization framework based on Grammatical Evolution (GE) which is able to efficiently find the best cache configurations for a given set of benchmark applications. This metaheuristic allows an important reduction of the optimization runtime obtaining good results in a low number of generations. Besides, this reduction is also increased due to the efficient storage of evaluated caches. Moreover, we selected GE because the plasticity of the grammar eases the creation of phenotypes that form the call to the cache simulator required for the evaluation of the different configurations. Experimental results for the Mediabench suite show that our proposal is able to find cache configurations that obtain an average improvement of $62\%$ versus a real world baseline configuration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
