PFS - Reliability Assessment of Neural Networks in GPUs

Autor:	Guerrero-Balaguera, Juan-David
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Microelectronics Convolutional Neural Networks Graphics Processing Units (GPUs) Reliability evaluation Spinitronics Integrated circuits Microelectrònica Circuits integrats Espintrònica Artificial Neural Networks Enginyeria electrònica::Microelectrònica [Àrees temàtiques de la UPC]
Zdroj:	UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC)
Popis:	Currently, Deep learning and especially Convolutional Neural Networks (CNNs) have become a fundamental computational approach applied in a wide range of domains, including some safety-critical applications (e.g., automotive, robotics, and healthcare equipment). Therefore, the reliability evaluation of those computational systems is mandatory. The reliability evaluation of CNNs is performed by fault injection campaigns at different levels of abstraction, from the application level down to the hardware level. Many works have focused their effort on evaluating the reliability of neural networks in the presence of transient faults. However, the effects of permanent faults have been investigated at the application level, only, e.g., targeting the parameters of the network. This paper presents the ongoing work on the reliability evaluation of CNNs targeting permanent faults in GPU devices, considering different fault injections levels. Our preliminary results show that the fault injections performed at the application level generate more optimistic results than considering an architectural level fault injection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::47b07c4aafe67d379903feaaf7d01583 https://hdl.handle.net/2117/369976 Zobrazit plný text záznamu