Exploring the Impact of Variability in Resistance Distributions of RRAM on the Prediction Accuracy of Deep Learning Neural Networks

Abstract

In this work, we explore the use of the resistive random access memory (RRAM) device as a synapse for mimicking the trained weights linking neurons in a deep learning neural network (DNN) (AlexNet). The RRAM devices were fabricated in-house and subjected to 1000 bipolar read-write cycles to measure the resistances recorded for Logic-0 and Logic-1 (we demonstrate the feasibility of achieving eight discrete resistance states in the same device depending on the RESET stop voltage). DNN simulations have been performed to compare the relative error between the output of AlexNet Layer 1 (Convolution) implemented with the standard backpropagation (BP) algorithm trained weights versus the weights that are encoded using the measured resistance distributions from RRAM. The IMAGENET dataset is used for classification purpose here. We focus only on the Layer 1 weights in the AlexNet framework with 11 × 11 × 96 filters values coded into a binary floating point and substituted with the RRAM resistance values corresponding to Logic-0 and Logic-1. The impact of variability in the resistance states of RRAM for the low and high resistance states on the accuracy of image classification is studied by formulating a look-up table (LUT) for the RRAM (from measured I-V data) and comparing the convolution computation output of AlexNet Layer 1 with the standard outputs from the BP-based pre-trained weights. This is one of the first studies dedicated to exploring the impact of RRAM device resistance variability on the prediction accuracy of a convolutional neural network (CNN) on an AlexNet platform through a framework that requires limited actual device switching test data.

Proton-Assisted Redox-Based Three-Terminal Memristor for Synaptic Device Applications

“…These IV characteristics are typically observed in filamentarytype of RRAM devices. 34,35 Under the 3T operating mode, the writing (blue arrow) and reading (black arrow) paths are decoupled. To initiate the switching operation of the O-3TM, a forming process is required to induce W channel oxidation under an external electric field.…”

Computational Failure Analysis of In-Memory RRAM Architecture for Pattern Classification CNN Circuits

“…4(c). As proposed in our past work [35], the normal resistance distribution of is encoded into the mantissa part of CNN trained weights as logic-0 with LRS data and logic-1 with HRS data (based on a threshold resistance value, RTH, i.e. if R < RTH, it is logic "0" and if R > RTH, it is logic "1") and both HRS and LRS resistance data are extracted from the extrapolated data set.…”

A novel CapsNet neural network based on MobileNetV2 structure for robot image classification

“…However, in the environment of missile-borne terminal, there are high requirements on network and hardware, which requires the network to maintain lightweight and good embedding performance on the basis of ensuring recognition accuracy. After AlexNet achieves a qualitative leap in the classification accuracy of ImageNet large data set (Prabhu et al, 2020), the development trend of convolutional neural network is complicated and the number of convolutional layers is increasing.…”