  Synaptic memory consolidation has been heralded as one of the key mechanisms
for supporting continual learning in neuromorphic Artificial Intelligence (AI)
systems. Here we report that a Fowler-Nordheim (FN) quantum-tunneling device
can implement synaptic memory consolidation similar to what can be achieved by
algorithmic consolidation models like the cascade and the elastic weight
consolidation (EWC) models. The proposed FN-synapse not only stores the
synaptic weight but also stores the synapse's historical usage statistic on the
device itself. We also show that the operation of the FN-synapse is
near-optimal in terms of the synaptic lifetime and we demonstrate that a
network comprising FN-synapses outperforms a comparable EWC network for a small
benchmark continual learning task. With an energy footprint of femtojoules per
synaptic update, we believe that the proposed FN-synapse provides an
ultra-energy-efficient approach for implementing both synaptic memory
consolidation and persistent learning.
