Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers

Danilo Pau, Davide Denaro, Luigi Zambrano, Giulia Crocioni, Giambattista Gruosso, Giuseppe Di Giore

Keywords: Microcontrollers, Neural Networks, LiIon Battery, Electric Vehicles, Machine Learning, embedded software, CAN networks

TL;DR: Can intrusion detection and battery charge prediction use cases automatically mapped on Automotive grade MCUs

Abstract:

Neural networks and Machine Learning in general, represent today
one of the greatest expectations for the realization of models
that can serve to determine the behavior and operation of different
physical systems. Undoubtedly the calculation resources necessary
for the training and the realization of the model are great especially
if linked to the amount of data needed to detect the salient parameters
of the model. At the same time, the models so obtained can be
integrated on embedded systems, thanks to TinyML technologies,
allowing to work exactly where the physical phenomena to analyze
happen. In the consumer and industrial world these technologies
have taken hold, and are also watched with interest by other sectors
such as the automotiveworld. In this articlewe present a framework
for the implementation of models based on neural networks on
automotive family microprocessors, demonstrating their efficiency
in two typical applications of the vehicle world: intrusion detection
on the CAN bus communication network and the determination of
the residual capacity of batteries for electric vehicles.