  Over the last years the rapid growth Machine Learning (ML) inference
applications deployed on the Edge is rapidly increasing. Recent Internet of
Things (IoT) devices and microcontrollers (MCUs), become more and more
mainstream in everyday activities. In this work we focus on the family of STM32
MCUs. We propose a novel methodology for CNN deployment on the STM32 family,
focusing on power optimization through effective clocking exploration and
configuration and decoupled access-execute convolution kernel execution. Our
approach is enhanced with optimization of the power consumption through Dynamic
Voltage and Frequency Scaling (DVFS) under various latency constraints,
composing an NP-complete optimization problem. We compare our approach against
the state-of-the-art TinyEngine inference engine, as well as TinyEngine coupled
with power-saving modes of the STM32 MCUs, indicating that we can achieve up to
25.2% less energy consumption for varying QoS levels.
