  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.
