Memcapacitive neural networks
This work addresses energy efficiency in neuromorphic computing for AI hardware, but it appears incremental as it builds on existing memristive approaches.
The authors tackled the problem of energy consumption in neuromorphic computing by proposing memcapacitive systems as synapses in artificial neural networks, demonstrating that they can realize spike-timing-dependent plasticity and serve as a low-energy alternative to memristive synapses.
We show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural networks. As an example of our approach, we discuss the architecture of an integrate-and-fire neural network based on memcapacitive synapses. Moreover, we demonstrate that the spike-timing-dependent plasticity can be simply realized with some of these devices. Memcapacitive synapses are a low-energy alternative to memristive synapses for neuromorphic computation.