The Impact of Precision Bitwidth on the Soft Error Reliability of the MobileNet Network

Machine learning (ML) algorithms are being incorporated in resource-constrained IoT platforms, which typically rely on reduced memory footprint and low performance processors. While performance improvement, customized, and reduced-precision implementations of such algorithms have been studied extensively, their susceptibility to soft errors caused by radiation particles is still an open question. This work contributes by investigating the impact of precision bitwidth on the soft error reliability of the MobileNet convolutional neural network (CNN) when executed on an Arm Cortex-M processor. Results obtained from more than 500k fault injections show that the soft error reliability varies depending on the precision bitwidth of the convolutional layers.