M
Matteo Sonza Reorda
Researcher at Polytechnic University of Turin
Publications - 340
Citations - 5043
Matteo Sonza Reorda is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Fault coverage & Automatic test pattern generation. The author has an hindex of 32, co-authored 295 publications receiving 4525 citations. Previous affiliations of Matteo Sonza Reorda include University of California, Riverside & NXP Semiconductors.
Papers
More filters
Proceedings ArticleDOI
Special Session: AutoSoC - A Suite of Open-Source Automotive SoC Benchmarks
Felipe Augusto da Silva,Ahmet Cagri Bagbaba,Annachiara Ruospo,Riccardo Mariani,Ghani Kanawati,Ernesto Sanchez,Matteo Sonza Reorda,Maksim Jenihhin,Said Hamdioui,Christian Sauer +9 more
TL;DR: The objective is to provide researchers with an industrial-grade automotive SoC that includes all essential components, is fully customizable, and enables analysis of functional safety solutions and automotive SoCs configurations.
Journal ArticleDOI
Evaluating the radiation sensitivity of GPGPU caches: New algorithms and experimental results
TL;DR: This paper presents three new algorithms designed to support radiation experiments aimed at evaluating the radiation sensitivity of GPGPU data caches and shared memory with particular emphasis on the shared memory and on the L1 and L2 data caches.
Proceedings ArticleDOI
Assessing Convolutional Neural Networks Reliability through Statistical Fault Injections
Annachiara Ruospo,G. Gavarini,Corrado De Sio,J. Guerrero,Luca Sterpone,Matteo Sonza Reorda,Ernesto Sanchez,J. Aribido,Jyotika Athavale +8 more
TL;DR: In this article , the authors describe how to correctly specify statistical FIs for Convolutional Neural Networks, and propose a data analysis on the CNN parameters that drastically reduces the number of FIs needed to achieve statistically significant results without compromising the validity of the proposed method.
Journal ArticleDOI
A Novel Sequence Generation Approach to Diagnose Faults in Reconfigurable Scan Networks
TL;DR: A method for generating stimuli to precisely identify permanent high-level faults in a IEEE 1687 reconfigurable scan chain is proposed: the system is modeled as a finite state automaton where faults correspond to multiple incorrect transitions; then, a dynamic greedy algorithm is used to select a sequence of inputs able to distinguish between all possible faults.