On-device Synaptic Memory Consolidation using Fowler-Nordheim Quantum-tunneling

Abstract

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 nearoptimal in terms of the synaptic life-time and we demonstrate that a network comprising FN-synapses outperforms a comparable EWC network for a small benchmark continual learning task. With an energy foot-print of femtojoules per synaptic update, we believe that the proposed FN-synapse provides an ultra-energy-efficient approch for implementing both synaptic memory consolidation and persistent learning.