LO³-ML (Low-Power Low-Memory Low-Cost ECG Signal Analysis using Machine Learning Algorithms) is a joint project between Fraunhofer IIS and Friedrich-Alexander-Universität Erlangen-Nürnberg. It is funded as part of the challenge for disruptive innovations in energy-efficient AI hardware initiated by the German Federal Ministry of Education and Research (BMBF). The goal of the project is to design a highly energy efficient ASIC (application-specific integrated circuit) for classification of atrial fibrillation in an ECG signal using neural networks by heavily optimizing neural network topologies and specifically adapting hardware architectures. To reduce the energy consumption to a minimum, Resistive Random Access Memory (RRAM) is applied as non-volatile, ternary parameter storage inside the chip.
More information about the innovation competition can be found here.