  Deploying Deep Neural Networks (DNNs) on microcontrollers (TinyML) is a
common trend to process the increasing amount of sensor data generated at the
edge, but in practice, resource and latency constraints make it difficult to
find optimal DNN candidates. Neural Architecture Search (NAS) is an excellent
approach to automate this search and can easily be combined with DNN
compression techniques commonly used in TinyML. However, many NAS techniques
are not only computationally expensive, especially hyperparameter optimization
(HPO), but also often focus on optimizing only a single objective, e.g.,
maximizing accuracy, without considering additional objectives such as memory
consumption or computational complexity of a DNN, which are key to making
deployment at the edge feasible. In this paper, we propose a novel NAS strategy
for TinyML based on Multi-Objective Bayesian optimization (MOBOpt) and an
ensemble of competing parametric policies trained using Augmented Random Search
(ARS) Reinforcement Learning (RL) agents. Our methodology aims at efficiently
finding tradeoffs between a DNN's predictive accuracy, memory consumption on a
given target system, and computational complexity. Our experiments show that we
outperform existing MOBOpt approaches consistently on different data sets and
architectures such as ResNet-18 and MobileNetV3.
