Sergiu-Mohamed Abed
Reliability assessment and software-based hardening of a hyperspectral image classifier for GPUs.
Rel. Josie Esteban Rodriguez Condia, Matteo Sonza Reorda, Juan David Guerrero Balaguera. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
Over the last few years, artificial intelligence (AI) has been adopted across many domains and sectors. One such domain is Edge Computing, where Internet of Things (IoT) devices are now being designed to handle neural networks to provide computation at the source of data to reduce latency and throughput on the network. In doing so, artificial intelligence now faces a new challenge, which is frequently encountered in embedded systems deployed in uncontrolled and rough environments: reliability. Many methods have been studied to assess the reliability of neural network models. However, more research is still needed to understand the effects of faults at the hardware level on the performance of neural networks.
This thesis focuses on the impact of transient faults at the device hardware on the performance of a Hyperspectral Image Classifier
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