memristor nanodevices for Artificial Neural Networks is a famous recent example
A recent example of such dynamics from device to architecture is the
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
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 Ám2.
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.