Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance while minimizing resource consumption. Since this combines multi-objective (MO) optimization with accuracy and energy consumption as potentially complementary objectives, we propose to combine state-of-the-art multi-fidelity (MF) HPO with multi-objective optimization. Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost. Overall, our method accelerates efficient model development while enabling sustainable AI applications.

翻译：深度学习（DL）通过从大型数据集中提取复杂模式，推动了多个领域的发展。然而，深度学习模型的计算需求带来了环境与资源方面的挑战。深度移位神经网络（DSNNs）通过利用移位操作降低推理时的计算复杂度，为此提供了一种解决方案。借鉴标准深度神经网络的洞见，我们致力于通过自动化机器学习技术充分挖掘DSNNs的潜力。本文研究超参数优化对最大化DSNN性能并最小化资源消耗的影响。由于这结合了以准确率和能耗为潜在互补目标的多目标优化，我们提出将最先进的多保真度超参数优化与多目标优化相结合。实验结果表明，该方法能有效生成准确率超过80%且计算成本低的模型。总体而言，我们的方法在加速高效模型开发的同时，实现了可持续的人工智能应用。