Efficient Transformer-based Hyper-parameter Optimization for
  Resource-constrained IoT Environments
Ibrahim Shaer Soodeh Nikan Abdallah Shami
The hyper-parameter optimization (HPO) process is imperative for finding the
best-performing Convolutional Neural Networks (CNNs). The automation process of
HPO is characterized by its sizable computational footprint and its lack of
transparency; both important factors in a resource-constrained Internet of
Things (IoT) environment. In this paper, we address these problems by proposing
a novel approach that combines transformer architecture and actor-critic
Reinforcement Learning (RL) model, TRL-HPO, equipped with multi-headed
attention that enables parallelization and progressive generation of layers.
These assumptions are founded empirically by evaluating TRL-HPO on the MNIST
dataset and comparing it with state-of-the-art approaches that build CNN models
from scratch. The results show that TRL-HPO outperforms the classification
results of these approaches by 6.8% within the same time frame, demonstrating
the efficiency of TRL-HPO for the HPO process. The analysis of the results
identifies the main culprit for performance degradation attributed to stacking
fully connected layers. This paper identifies new avenues for improving
RL-based HPO processes in resource-constrained environments.