J
Jean-Luc Gaudiot
Researcher at University of California, Irvine
Publications - 285
Citations - 3485
Jean-Luc Gaudiot is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Thread (computing) & Scheduling (computing). The author has an hindex of 25, co-authored 277 publications receiving 3027 citations. Previous affiliations of Jean-Luc Gaudiot include University of California, Berkeley & IEEE Computer Society.
Papers
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Journal ArticleDOI
Network resilience: a measure of network fault tolerance
Walid Najjar,Jean-Luc Gaudiot +1 more
TL;DR: The authors derive an analytical approximation to the disconnection probability and verify it with a Monte Carlo simulation, on the basis of which the measures of network resilience and relative network resilience are proposed as probabilistic measures ofnetwork fault tolerance.
Journal ArticleDOI
Secure Data Storage and Recovery in Industrial Blockchain Network Environments
TL;DR: Experimental results show that the proposed scheme improves the repair rate of multinode data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.
Parallel and distributed systems
James D. Isaak,Sorel Reisman,Jeffrey Voas,Elizabeth Burd,Sattupathu V. Sankaran,David Alan Grier,James W. Moore,John W. Walz,Frank E. Ferrante,Michael R. Williams,Stephen L. Diamond,Carl K. Chang,Piere Bourque,André Ivanov,Phillip A. Laplante,Itaru Mimura,Jon G. Rokne,Christina M. Schober,Elisa Bertino,George V. Cybenko,David S. Ebert,David A. Grier,Hironori Kasahara,Steven L. Tanimoto,Thomas M. Conte,Jean-Luc Gaudiot,Luis Kun,Angela R. Burgess,John H. Miller +28 more
Journal ArticleDOI
Computer Architectures for Autonomous Driving
TL;DR: To enable autonomous driving, a computing stack must simultaneously ensure high performance, consume minimal power, and have low thermal dissipation—all at an acceptable cost.
Journal ArticleDOI
Enabling Deep Learning on IoT Devices
TL;DR: Two ways to successfully integrate deep learning with low-power IoT products are explored.