Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and autonomic scaling. Existing approaches to understanding the performance of hardware largely focus around benchmarking -- leveraging standardised workloads which seek to be representative of an end-user's needs. Two key challenges are present; benchmark workloads may not be representative of an end-user's workload, and benchmark scores are not easily obtained for all hardware. Within this paper, we demonstrate the potential to build Deep Learning models to predict benchmark scores for unseen hardware. We undertake our evaluation with the openly available SPEC 2017 benchmark results. We evaluate three different networks, one fully-connected network along with two Convolutional Neural Networks (one bespoke and one ResNet inspired) and demonstrate impressive $R^2$ scores of 0.96, 0.98 and 0.94 respectively.

翻译：预测计算硬件的性能与能耗对许多现代应用至关重要，这将为采购决策、部署决策及自主扩展提供依据。现有理解硬件性能的方法主要集中于基准测试——利用标准化工作负载来模拟最终用户的需求。目前存在两大关键挑战：基准测试工作负载可能无法代表最终用户的实际工作负载，且难以获取所有硬件的基准测试分数。本文展示了构建深度学习模型以预测未见硬件基准测试分数的潜力。我们基于公开可用的SPEC 2017基准测试结果进行评估。我们评估了三种不同的网络：一个全连接网络及两个卷积神经网络（一个定制网络与一个ResNet启发式网络），分别取得了0.96、0.98和0.94的惊人R²分数。