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.

深度学习已经通过从大规模数据集中提取复杂模式推动了各个领域的发展。本研究关注于利用自动机器学习技术充分发挥深度位移神经网络的潜力，并通过超参数优化来提高性能并降低资源消耗，实现多目标优化和多保真度。实验结果表明，我们的方法有效，能够获得 80% 以上的准确率并具有较低的计算成本，加速了高效模型开发并支持可持续的人工智能应用。