Software systems often have numerous configuration options that can be
adjusted to meet different performance requirements. However, understanding the
combined impact of these options on performance is often challenging,
especially with limited real-world data. To tackle this issue, deep learning
techniques have gained popularity due to their ability to capture complex
relationships even with limited samples. This thesis begins with a systematic
literature review of deep learning techniques in configuration performance
modeling, analyzing 85 primary papers out of 948 searched papers. It identifies
knowledge gaps and sets three objectives for the thesis. The first knowledge
gap is the lack of understanding about which encoding scheme is better and in
what circumstances. To address this, the thesis conducts an empirical study
comparing three popular encoding schemes. Actionable suggestions are provided
to support more reliable decisions. Another knowledge gap is the sparsity
inherited from the configuration landscape. To handle this, the thesis proposes
a model-agnostic and sparsity-robust framework called DaL, which uses a
"divide-and-learn" approach. DaL outperforms state-of-the-art approaches in
accuracy improvement across various real-world systems. The thesis also
addresses the limitation of predicting under static environments by proposing a
sequential meta-learning framework called SeMPL. Unlike traditional
meta-learning frameworks, SeMPL trains meta-environments in a specialized
order, resulting in significantly improved prediction accuracy in
multi-environment scenarios. Overall, the thesis identifies and addresses
critical knowledge gaps in deep performance learning, significantly advancing
the accuracy of performance prediction.

通过系统文献综述和实证研究，本论文提出了深度学习技术在配置性能建模方面的知识缺口，并通过比较编码方案、提出稀疏性鲁棒框架 DaL 和顺序元学习框架 SeMPL，显著提高了性能预测的准确性。

推进边界：专注于深度配置性能学习

Pushing the Boundary: Specialising Deep Configuration Performance  Learning

As spiking neural networks (SNNs) are event-driven, energy efficiency is
higher than conventional artificial neural networks (ANNs). Since SNN delivers
data through discrete spikes, it is difficult to use gradient methods for
training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN
counterparts, pre-trained ANNs are converted to SNNs (ANN-to-SNN conversion).
During the conversion, encoding activations of ANNs to a set of spikes in SNNs
is crucial for minimizing the conversion loss. In this work, we propose a
single-spike phase coding as an encoding scheme that minimizes the number of
spikes to transfer data between SNN layers. To minimize the encoding error due
to single-spike approximation in phase coding, threshold shift and base
manipulation are proposed. Without any additional retraining or architectural
constraints on ANNs, the proposed conversion method does not lose inference
accuracy (0.58% on average) verified on three convolutional neural networks
(CNNs) with CIFAR and ImageNet datasets.In addition, graph convolutional
networks (GCNs) are converted to SNNs successfully with an average accuracy
loss of 0.90%.Most importantly, the energy efficiency of our SNN improves by
4.6~17.3 X compared to the ANN baseline.

本研究提出了一种单脉冲相位编码方案，用于将预训练人工神经网络（ANN）转换为脉冲神经网络（SNN），从而实现较高的精确度和能效。