Deep learning has irreversibly changed and drastically enhanced how we process information. The rapidly increasing computation time and energy costs required to train ever larger AI models make it evident that the future of artificial intelligence depends on realizing fast and energy-efficient processors. With the slowdown in transistor scaling and the diminishing returns expected from future CMOS, the concept of analog computing has been put forward as an alternative. Analog neural networks process information that is stored locally and in a fully-parallel manner in the analog domain using physical device properties instead of conventional Boolean arithmetic. This presentation will give an overview of analog neural network and the underlying device technologies to implement them.