  Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the
design of deep learning architectures, tailored specifically to a given target
hardware platform. Yet, these techniques demand substantial computational
resources, primarily due to the expensive process of assessing the performance
of identified architectures. To alleviate this problem, a recent direction in
the literature has employed representation similarity metric for efficiently
evaluating architecture performance. Nonetheless, since it is inherently a
single objective method, it requires multiple runs to identify the optimal
architecture set satisfying the diverse hardware cost constraints, thereby
increasing the search cost. Furthermore, simply converting the single objective
into a multi-objective approach results in an under-explored architectural
search space. In this study, we propose a Multi-Objective method to address the
HW-NAS problem, called MO-HDNAS, to identify the trade-off set of architectures
in a single run with low computational cost. This is achieved by optimizing
three objectives: maximizing the representation similarity metric, minimizing
hardware cost, and maximizing the hardware cost diversity. The third objective,
i.e. hardware cost diversity, is used to facilitate a better exploration of the
architecture search space. Experimental results demonstrate the effectiveness
of our proposed method in efficiently addressing the HW-NAS problem across six
edge devices for the image classification task.
