Abstract
The multilevel resistive random access memory (RRAM)-based synaptic array can enable parallel computations of vector-matrix multiplication for machine learning inference acceleration; however, any conductance drift of the cell may induce an inference accuracy drop because the analog current is summed up along the column. In this article, the read disturb-induced conductance drift characteristic is statistically measured on a test vehicle based on 2-bit HfO2 RRAM array. The drift behavior of four states is empirically modeled by a vertical and lateral filament growth mechanism. Furthermore, a bipolar read scheme is proposed and tested to enhance the resilience against the read disturb. The modeled read disturb and proposed compensation scheme are incorporated into a VGG-like convolutional neural network for CIFAR-10 data set inference.
Original language | English (US) |
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Article number | 9069908 |
Pages (from-to) | 2318-2323 |
Number of pages | 6 |
Journal | IEEE Transactions on Electron Devices |
Volume | 67 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2020 |
Keywords
- In-memory computing
- Multilevel resistive random access memory (RRAM)
- Neural network
- Read disturb
- Reliability
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering