  The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in
the automotive industry and, recently, also in applications that involve
machine learning tasks. Yet, these applications are mainly engineered manually,
and only little tool support exists for bringing neural networks to TriCore
microcontrollers. Thus, we propose OpTC, an end-to-end toolchain for automatic
compression, conversion, code generation, and deployment of neural networks on
TC3xx microcontrollers. OpTC supports various types of neural networks and
provides compression using layer-wise pruning based on sensitivity analysis for
a given neural network. The flexibility in supporting different types of neural
networks, such as multi-layer perceptrons (MLP), convolutional neural networks
(CNN), and recurrent neural networks (RNN), is shown in case studies for a
TC387 microcontroller. Automotive applications for predicting the temperature
in electric motors and detecting anomalies are thereby used to demonstrate the
effectiveness and the wide range of applications supported by OpTC.
