In the machine learning ecosystem, hardware selection is often regarded as a
mere utility, overshadowed by the spotlight on algorithms and data. This
oversight is particularly problematic in contexts like ML-as-a-service
platforms, where users often lack control over the hardware used for model
deployment. How does the choice of hardware impact generalization properties?
This paper investigates the influence of hardware on the delicate balance
between model performance and fairness. We demonstrate that hardware choices
can exacerbate existing disparities, attributing these discrepancies to
variations in gradient flows and loss surfaces across different demographic
groups. Through both theoretical and empirical analysis, the paper not only
identifies the underlying factors but also proposes an effective strategy for
mitigating hardware-induced performance imbalances.

在机器学习生态系统中，硬件选择经常被认为只是一个辅助工具，而算法和数据则是关注重点。然而，在诸如机器学习即服务平台这样的环境中，用户通常无法控制模型部署所使用的硬件，这种忽视尤其成问题。本文调查了硬件选择对模型性能和公平性之间微妙平衡的影响。我们通过理论和实证分析，不仅找出了其中的潜在因素，还提出了一种有效的策略来缓解硬件引起的性能不平衡。

关于机器学习中硬件选择的公平性影响

On The Fairness Impacts of Hardware Selection in Machine Learning

Deep learning is rapidly becoming a go-to tool for many artificial
intelligence problems due to its ability to outperform other approaches and
even humans at many problems. Despite its popularity we are still unable to
accurately predict the time it will take to train a deep learning network to
solve a given problem. This training time can be seen as the product of the
training time per epoch and the number of epochs which need to be performed to
reach the desired level of accuracy. Some work has been carried out to predict
the training time for an epoch -- most have been based around the assumption
that the training time is linearly related to the number of floating point
operations required. However, this relationship is not true and becomes
exacerbated in cases where other activities start to dominate the execution
time. Such as the time to load data from memory or loss of performance due to
non-optimal parallel execution. In this work we propose an alternative approach
in which we train a deep learning network to predict the execution time for
parts of a deep learning network. Timings for these individual parts can then
be combined to provide a prediction for the whole execution time. This has
advantages over linear approaches as it can model more complex scenarios. But,
also, it has the ability to predict execution times for scenarios unseen in the
training data. Therefore, our approach can be used not only to infer the
execution time for a batch, or entire epoch, but it can also support making a
well-informed choice for the appropriate hardware and model.

本文提出了一种针对深度学习神经网络训练时间预测的方法，通过训练神经网络预测每部分的执行时间，从而得出整体的深度学习网络训练时间，可以用于推断每个批次或整个 epoch 的执行时间，也可以支持选择适当的硬件和模型。