Memory Technologies for Neural Networks

Synapses, the most numerous elements of neural networks, are memory devices. Similarly to traditional memory applications, device density is one of the most essential metrics for large-scale artificial neural networks. This application, however, imposes a number of additional requirements, such as the continuous change of the memory state, so that novel engineering approaches are required. In this paper, we briefly review our recent efforts at addressing these needs. We start by reviewing the CrossNet concept, which was conceived to address major challenges of artificial neural networks. We then discuss the recent progress toward CrossNet implementation, in particular the experimental results for simple networks with crossbar-integrated resistive switching (memristive) metal oxide devices. Finally, we review preliminary results on redesigning commercial-grade embedded NOR flash memories to enable individual cell tuning. While NOR flash memories are less dense then memristor crossbars, their technology is much more mature and ready for the development of large-scale neural networks.