  In this paper we present a synaptic array that uses dynamical states to
implement an analog memory for energy-efficient training of machine learning
(ML) systems. Each of the analog memory elements is a micro-dynamical system
that is driven by the physics of Fowler-Nordheim (FN) quantum tunneling,
whereas the system level learning modulates the state trajectory of the memory
ensembles towards the optimal solution. We show that the extrinsic energy
required for modulation can be matched to the dynamics of learning and weight
decay leading to a significant reduction in the energy-dissipated during ML
training. With the energy-dissipation as low as 5 fJ per memory update and a
programming resolution up to 14 bits, the proposed synapse array could be used
to address the energy-efficiency imbalance between the training and the
inference phases observed in artificial intelligence (AI) systems.
