A Survey on Deep Learning Resilience Assessment Methodologies

Autor: Annachiara Ruospo, Ernesto Sanchez, Lucas Matana Luza, Luigi Dilillo, Marcello Traiola, Alberto Bosio

Publikováno v: Computer
Computer, 2023, 56, pp.57-66. ⟨10.1109/MC.2022.3217841⟩

International audience; Deep Learning (DL) applications are gaining increasing interest in the industry and academia for their outstanding computational capabilities. Indeed, they have found successful applications in various areas and domains such a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe77c451d906e07fc429717fa472b3c3
https://doi.org/10.1109/mc.2022.3217841

Selective Hardening of Critical Neurons in Deep Neural Networks

Autor: Annachiara Ruospo, Gabriele Gavarini, Ilaria Bragaglia, Marcello Traiola, Alberto Bosio, Ernesto Sanchez

Publikováno v: DDECS 2022-25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems
DDECS 2022-25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems, Apr 2022, Prague, Czech Republic. pp.136-141, ⟨10.1109/DDECS54261.2022.9770168⟩

International audience; In the literature, it is argued that Deep Neural Networks (DNNs) possess a certain degree of robustness mainly for two reasons: their distributed and parallel architecture, and their redundancy introduced due to over provision

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7f7cebf3e60ad9e8183680acd20d5c3
https://hal.inria.fr/hal-03888005/document

Emulating the Effects of Radiation-Induced Soft-Errors for the Reliability Assessment of Neural Networks

Autor: Luigi Dilillo, Carlo Cazzaniga, Annachiara Ruospo, Alberto Bosio, Daniel Soderstrom, Maria Kastriotou, Ernesto Sanchez, Lucas Matanaluza

Publikováno v: IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TETC.2021.3116999⟩

International audience; Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive models in machine learning. Recent studies have demonstrated that hardware faults induced by radiation fields, including cosmic rays, ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92ce93a1d8750deeb0a9765f433053e1
http://urn.fi/URN:NBN:fi:jyu-202302021604

Pros and Cons of Fault Injection Approaches for the Reliability Assessment of Deep Neural Networks

Autor: Annachiara Ruospo, Lucas Matana Luza, Alberto Bosio, Marcello Traiola, Luigi Dilillo, Ernesto Sanchez

Publikováno v: LATS 2021-IEEE 22nd Latin American Test Symposium
LATS 2021-IEEE 22nd Latin American Test Symposium, Oct 2021, Punta del Este, Uruguay. pp.1-5, ⟨10.1109/LATS53581.2021.9651807⟩
IEEE Latin-American Test Symposium (LATS 2021)
IEEE Latin-American Test Symposium (LATS 2021), Oct 2021, Punta del Este, Uruguay

International audience; In the last years, the adoption of Artificial Neural Networks (ANNs) in safety-critical applications has required an in-depth study of their reliability. For this reason, the research community has shown a growing interest in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0972e236925cd6c74d6f97eab89ab7f6
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03435567

A Benchmark Suite of RT-level Hardware Trojans for Pipelined Microprocessor Cores

Autor: Giovanni Squillero, Ernesto Sanchez, Annachiara Ruospo, Aleksa Damljanovic

Publikováno v: DDECS
2021 24th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS)

Recent trends in integrated circuits industry include decentralization of the production flow by involving different integration teams, third-party IP vendors and other untrusted entities. As a result, this is opening up a door to new types of attack

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db526c22d718e8f44a7122222655bd69
https://doi.org/10.1109/ddecs52668.2021.9417061