  Nowadays, Neural Networks represent a major expectation for the realization
of powerful Deep Learning algorithms, which can determine several physical
systems' behaviors and operations. Computational resources required for model,
training, and running are large, especially when related to the amount of data
that Neural Networks typically need to generalize. The latest TinyML
technologies allow integrating pre-trained models on embedded systems, allowing
making computing at the edge faster, cheaper, and safer. Although these
technologies originated in the consumer and industrial worlds, many sectors can
greatly benefit from them, such as the automotive industry. In this paper, we
present a framework for implementing Neural Network-based models on a family of
automotive Microcontrollers, showing their efficiency in two case studies
applied to vehicles: intrusion detection on the Controller Area Network bus and
residual capacity estimation in Lithium-Ion batteries, widely used in Electric
Vehicles.
