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.
Cite
@article{arxiv.2206.14581,
title = {On-device Synaptic Memory Consolidation using Fowler-Nordheim Quantum-tunneling},
author = {Mustafizur Rahman and Subhankar Bose and Shantanu Chakrabartty},
journal= {arXiv preprint arXiv:2206.14581},
year = {2022}
}