AllgemeinProject Team wins Award for Energy Efficient Neuromorphic Hardware Design
Creating an energy efficient chip, which can classify ECG signals into healthy and arrhythmical using neural networks, was the goal of the first challenge for disruptive innovations in energy-efficient AI hardware initiated by the German Federal Ministry of Education and Research (BMBF). The Project Team „Low-Power Low-Memory Low-Cost ECG signal analysis using ML algorithms (Lo3-ML)“ […]Creating an energy efficient chip, which can classify ECG signals into healthy and arrhythmical using neural networks, was the goal of the first challenge for disruptive innovations in energy-efficient AI hardware initiated by the German Federal Ministry of Education and Research (BMBF). The Project Team „Low-Power Low-Memory Low-Cost ECG signal analysis using ML algorithms (Lo3-ML)“ […]
