Efficient Deep Neural Network Accelerator Using Controlled Ferroelectric Domain Dynamics

TL;DR

This paper presents a novel DNN accelerator utilizing ferroelectric tunnel junctions for synaptic weights, demonstrating high accuracy and energy efficiency through controlled ferroelectric domain dynamics.

Contribution

It introduces a new hardware platform combining ferroelectric devices with algorithms for efficient neural network training and inference.

Findings

Achieved ~93% accuracy on MNIST with ferroelectric-based DNN.

Demonstrated ultrafast switching and low energy operation of ferroelectric devices.

Identified challenges in dynamic conductance range for device optimization.

Abstract

The current work reports an efficient deep neural network (DNN) accelerator where synaptic weight elements are controlled by ferroelectric domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices is used. In P(VDF-TrFE) based ferroelectric tunnel junctions, analog conductance states are measured using a custom pulsing protocol and associated custom circuits and array architectures for DNN training is simulated. Our results show precise control of polarization switching dynamics in multi-domain, polycrystalline ferroelectric thin films can produce considerable weight update linearity in metal-ferroelectric-semiconductor (MFS) tunnel junctions. Ultrafast switching and low junction current in these devices offer extremely energy efficient operation. Through an integrated platform of hardware development, characterization and modelling, we predict the available conductance range where linearity is expected under identical potentiating and depressing pulses for efficient DNN training and inference tasks. As an example, an analog crossbar based DNN accelerator with MFS junctions as synaptic weight elements showed ~ 93% training accuracy on large MNIST handwritten digit dataset while for cropped images, a 95% accuracy is achieved. One observed challenge is rather limited dynamic conductance range while operating under identical potentiating and depressing pulses below 1V. Investigation is underway for improving the dynamic conductance range without losing the weight update linearity.