AUTHOR=Li Yiyang , Xiao T. Patrick , Bennett Christopher H. , Isele Erik , Melianas Armantas , Tao Hanbo , Marinella Matthew J. , Salleo Alberto , Fuller Elliot J. , Talin A. Alec 

TITLE=In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.636127

DOI=10.3389/fnins.2021.636127

ISSN=1662-453X

ABSTRACT=In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analogue memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analogue states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher speed and energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3×3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with an outer product update. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance towards developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.