  Recent advancements in Artificial Intelligence (AI), driven by Neural
Networks (NN), demand innovative neural architecture designs, particularly
within the constrained environments of Internet of Things (IoT) systems, to
balance performance and efficiency. HW-aware Neural Architecture Search
(HW-aware NAS) emerges as an attractive strategy to automate the design of NN
using multi-objective optimization approaches, such as evolutionary algorithms.
However, the intricate relationship between NN design parameters and HW-aware
NAS optimization objectives remains an underexplored research area, overlooking
opportunities to effectively leverage this knowledge to guide the search
process accordingly. Furthermore, the large amount of evaluation data produced
during the search holds untapped potential for refining the optimization
strategy and improving the approximation of the Pareto front. Addressing these
issues, we propose SONATA, a self-adaptive evolutionary algorithm for HW-aware
NAS. Our method leverages adaptive evolutionary operators guided by the learned
importance of NN design parameters. Specifically, through tree-based surrogate
models and a Reinforcement Learning agent, we aspire to gather knowledge on
'How' and 'When' to evolve NN architectures. Comprehensive evaluations across
various NAS search spaces and hardware devices on the ImageNet-1k dataset have
shown the merit of SONATA with up to 0.25% improvement in accuracy and up to
2.42x gains in latency and energy. Our SONATA has seen up to sim$93.6% Pareto
dominance over the native NSGA-II, further stipulating the importance of
self-adaptive evolution operators in HW-aware NAS.
