An Adaptive Synaptic Array using Fowler-Nordheim Dynamic Analog Memory

TL;DR

This paper introduces an energy-efficient analog memory array based on Fowler-Nordheim quantum tunneling, enabling low-energy training of machine learning models with high resolution.

Contribution

It presents a novel synaptic array utilizing dynamical states driven by FN tunneling for energy-efficient ML training.

Findings

Energy dissipation as low as 5 fJ per update

Programming resolution up to 14 bits

Potential to balance energy use between training and inference

Abstract

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.