Artificial Neural Networks, in particular Deep Learning, drastically improve the way computers see, hear and interact with their users and environment. However, Deep Learning applications demand high processing and memory throughput resulting in enormous energy usage.
For this reason, the design of energy-efficient HW platforms remains a significant challenge. Neuromorphic computing architectures aim to reduce the energy cost of NN processing by placing memory and compute kernels tightly coupled together, hence reducing the number of costly data movements.
Resistive RAM (ReRAM) is an emerging non-volatile memory technology that offers excellent density, power efficiency and compatibility with the established CMOS design process. These qualities make it an ideal element for implementing novel neuromorphic computing platforms.