Impact of Thread Parallelism on the Soft Error Reliability of Convolution Neural Networks

Convolution neural networks (CNNs) have been incorporated into resource-constrained edge devices to intelligently manage and process local data coming from a variety of sensors. Thread parallelism has been used to boost the performance of neural networks, but only few works address the effect of these parallel modifications on the soft error reliability of underlying models running on edge devices. In this sense, this work aims to assess the soft error reliability of a multi-threaded version of a CNN model developed based on the Arm CMSIS-NN kernels. Results show that the developed threaded CNN model increases performance at the cost of low memory footprint overhead. Promoted multi-threaded CNN model also provides better soft error reliability w.r.t. the original sequential version.