Summary of Optc — a Toolchain For Deployment Of Neural Networks on Aurix Tc3xx Microcontrollers, by Christian Heidorn et al.
OpTC – A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers
by Christian Heidorn, Frank Hannig, Dominik Riedelbauch, Christoph Strohmeyer, Jürgen Teich
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed end-to-end toolchain, OpTC, enables automatic compression, conversion, code generation, and deployment of neural networks on TC3xx microcontrollers. This toolchain supports various types of neural networks, including multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). OpTC uses layer-wise pruning based on sensitivity analysis to compress neural networks for a given microcontroller. The effectiveness of OpTC is demonstrated in case studies using a TC387 microcontroller for automotive applications, such as predicting electric motor temperatures and detecting anomalies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OpTC is a new tool that helps people make machine learning models work with special microcontrollers used in cars. It can take different types of neural networks and make them smaller and faster to use on these microcontrollers. This is useful for things like predicting how hot car parts will get or finding problems before they happen. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Pruning » Rnn
