OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers

TL;DR

OpTC is an automated toolchain that streamlines the deployment of various neural network types on AURIX TC3xx microcontrollers, facilitating automotive applications with efficient compression and conversion techniques.

Contribution

It introduces OpTC, a comprehensive toolchain that automates neural network compression, conversion, and deployment specifically for AURIX TC3xx microcontrollers, supporting multiple neural network architectures.

Findings

Supports multiple neural network types like MLP, CNN, RNN

Demonstrates effectiveness in automotive temperature prediction

Enables anomaly detection applications

Abstract

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.