  Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to
automatically design the architecture of a neural network for a specific task
and target hardware. However, evaluating the performance of candidate
architectures is a key challenge in HW-NAS, as it requires significant
computational resources. To address this challenge, we propose an efficient
hardware-aware evolution-based NAS approach called HW-EvRSNAS. Our approach
re-frames the neural architecture search problem as finding an architecture
with performance similar to that of a reference model for a target hardware,
while adhering to a cost constraint for that hardware. This is achieved through
a representation similarity metric known as Representation Mutual Information
(RMI) employed as a proxy performance evaluator. It measures the mutual
information between the hidden layer representations of a reference model and
those of sampled architectures using a single training batch. We also use a
penalty term that penalizes the search process in proportion to how far an
architecture's hardware cost is from the desired hardware cost threshold. This
resulted in a significantly reduced search time compared to the literature that
reached up to 8000x speedups resulting in lower CO2 emissions. The proposed
approach is evaluated on two different search spaces while using lower
computational resources. Furthermore, our approach is thoroughly examined on
six different edge devices under various hardware cost constraints.
