memristor nanodevices for Artificial Neural Networks is a famous recent example

A recent example of such dynamics from device to architecture is the memristor, an analog, voltage-tunable nano-resistance with a memory effect. These resistive switching devices belong to the top 3 technologies foreseen to be able to replace CMOS in next generation binary memories. For this reason, their performances are now benefiting from a strong academic and industrial research effort. But memristors are not just interesting as binary memories.

Thanks to their non-volatility and tunability, memristors can emulate the synaptic behavior at the nano-scale. In particular, when they are driven by spiking neurons, they can implement a learning rule, called Spike Timing Dependent Plasticity. This last rule, which has been observed in biology, also allows non-supervised learning in artificial neural networks. This means that the network learns by itself to perform some tasks, like extracting particular features from a video.

This discovery has strongly contributed to revive research in large scale hardware artificial neural networks. Indeed this field is suffering from the difficulty to down-size CMOS synapses, currently limited to hundreds of µm² . As the network performances depend on its size and interconnectivity, replacing CMOS synapses by nanoscale memristor synapses would yield tremendous gains in terms of silicon area and energy consumption.