Using continuous statistical machine learning to enable high-speed performance prediction in hybrid instruction-/cycle-accurate instruction set simulators
Using continuous statistical machine learning to enable high-speed performance prediction in hybrid instruction-/cycle-accurate instruction set simulators
复制标题
使用连续统计机器学习在混合指令/周期精确指令集模拟器中实现高速性能预测
DOI：
10.1145/1629435.1629478
复制
发表时间：
2009
影响因子：
2.8
通讯作者：
Björn Franke
中科院分区：
文献类型：
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作者：
D. Powell;Björn Franke
功能指令集模拟器以高指令速率执行指令精确的基准模拟。然而，与速度较慢但周期精确的同类产品不同，由于硬件抽象级别较高，它们无法提供周期计数。在本文中，我们提出了一种基于统计机器学习的性能预测新方法，利用混合指令和周期精确模拟器。我们将连续机器学习的概念引入模拟，从而按需获取新的训练数据点并用于性能模型的动态更新。此外，我们还展示了如何调整统计回归以降低性能关键模拟期间这些更新的成本。对于对 ARC 750D 嵌入式处理器进行建模的最先进的模拟器，我们证明了我们的方法非常准确，平均误差 <2.5%，同时实现了约 10% 的加速。比基准周期精确模拟高出 50%。
Functional instruction set simulators perform instruction-accurate simulation of benchmarks at high instruction rates. Unlike their slower, but cycle-accurate counterparts however, they are not capable of providing cycle counts due to the higher level of hardware abstraction. In this paper we present a novel approach to performance prediction based on statistical machine learning utilizing a hybrid instruction- and cycle-accurate simulator. We introduce the concept of continuous machine learning to simulation whereby new training data points are acquired on demand and used for on-the-fly updates of the performance model. Furthermore, we show how statistical regression can be adapted to reduce the cost of these updates during a performance-critical simulation. For a state-of-the-art simulator modeling the ARC 750D embedded processor we demonstrate that our approach is highly accurate, with average error <2.5% while achieving a speed-up of approx. 50% over the baseline cycle-accurate simulation.
DOI：
--
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Franke, B
通讯作者：
Franke, B