Soft Error Reliability Assessment of Neural Networks on Resource-constrained IoT Devices
Geancarlo Abich, Jonas Gava, Ricardo Reis, Luciano Ost
Abstract
Machine learning (ML) algorithms have provided straightforward solutions to a wide range of applications. The high computational demand of such algorithms limits their adoption in resource-constrained devices, typically relying on reduced memory footprint and low-power components (e.g., processors). While performance improvement, customized, and reduced-precision implementations of ML algorithms have been studied extensively, their susceptibility to soft errors caused by radiation particles is still an open question. In this regard, this work contributes to the soft error reliability assessment of a convolutional neural network (CNN) developed based on the Arm CMSIS-NN library. Results show that the soft error reliability varies depending on the instruction set architecture and the layer where the faults are injected.