It becomes increasingly difficult to improve the speed-energy efficiency of traditional digital processors because of limitations in transistor scaling and the von Neumann architecture. To address this issue, computing systems augmented with emerging devices, in particular resistance switches, offer an attractive solution. Built into large-scale crossbar arrays, resistance switches perform in-memory computing by utilizing physical laws, such as Ohm’s law for multiplication and Kirchhoff’s current law for accumulation. The current readout at all columns is finished simultaneously regardless of the array size, offering a massive parallelism and hence superior computing throughput. The ability to directly interface with analog signals from sensors, without analog/digital conversion, could further reduce the processing time and energy overhead. In this overview lecture, a number of emerging memory devices will be introduced and compared first, including phase change memory, magnetoresistance random access memory, resistance random access memory, and ferroelectric memory. Challenges and solutions in the integration of these devices into large-scale arrays, such as the scability and manufacturability, will then be discussed. Finally, the operation of the arrays and the implementation of multilayer neural networks for machine intelligence applications will be demonstrated.