University of California

King Abdullah University of Science and Technology

Unist-academy Industry Research Corporation

Jong Eun Lee

Ahmed Mohamed Eltawil

Fadi Joseph Kurdahi

Mohammed Fouda

Su Gil Lee

Gi Ju Jung

A stuck-at fault mitigation method for resistive random access memory (ReRAM)-based deep learning accelerators, includes: confirming a distorted output value (Y0) due to a stuck-at fault (SAF) by using a correction data set in a pre-trained deep learning network, by means of ReRAM-based deep learning accelerator hardware; updating an average (&#x3bc;) and a standard deviation (&#x3c3;) of a batch normalization (BN) layer by using the distorted output value (Y0), by means of the ReRAM-based deep learning accelerator hardware; folding the batch normalization (BN) layer in which the average (&#x3bc;) and the standard deviation (&#x3c3;) are updated into a convolution layer or a fully-connected layer, by means of the ReRAM-based deep learning accelerator hardware; and deriving a normal output value (Y1) by using the deep learning network in which the batch normalization (BN) layer is folded, by means of the ReRAM-based deep learning accelerator hardware.

Stuck-at fault mitigation method for ReRAM-based deep learning accelerators

Growing community of inventors

Ulsan, South Korea

Jong Eun Lee