Optimization and Toolchain for Embedding AI
Artificial Intelligence (AI) methods have quickly progressed from research to productive applications in recent years. Typical AI models (e.g., deep neural networks) yield high memory demands and computational efforts for training and when making predictions during operation. This is opposed to the typically limited resources of embedded controllers used in automotive or industrial applications. To comply with these limitations, AI models must be streamlined on different levels to be applicable to a given specific embedded target hardware, e.g., by architecture and feature selection, pruning, and other compression techniques. Currently, model adaptation to fit the target hardware is achieved by iterative, manual changes in a “trial-and-error” manner: the model is designed, trained, and compiled to the target hardware while applying different optimization techniques. The model is then checked for compliance with the hardware constraints, and the cycle is repeated if necessary. This approach is time-consuming and error-prone.
Therefore, this project, funded by the Schaeffler Hub for Advanced Research at Friedrich-Alexander-Universität Erlangen-Nürnberg (SHARE at FAU), seeks to establish guidelines for hardware selection and a systematic toolchain for optimizing and embedding AI in order to reduce the current efforts of porting machine learning models to automotive and industrial devices.