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Gianluca Palermo

Researcher at Polytechnic University of Milan

Publications -  187
Citations -  3191

Gianluca Palermo is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Design space exploration & Compiler. The author has an hindex of 30, co-authored 176 publications receiving 2879 citations. Previous affiliations of Gianluca Palermo include STMicroelectronics.

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Proceedings ArticleDOI

AES power attack based on induced cache miss and countermeasure

TL;DR: A new attack against a software implementation of the Advanced Encryption Standard, aimed at flushing elements of the SBOX from the cache, thus inducing a cache miss during the encryption phase, which can be used to recover part of the secret key.
Journal ArticleDOI

ReSPIR: A Response Surface-Based Pareto Iterative Refinement for Application-Specific Design Space Exploration

TL;DR: An efficient DSE methodology for application-specific MPSoC is proposed that is efficient in the sense that it is capable of finding a set of good candidate architecture configurations by minimizing the number of simulations to be executed.
Journal ArticleDOI

Secure Memory Accesses on Networks-on-Chip

TL;DR: This paper presents a secure NoC architecture composed of a set of data protection units (DPUs) implemented within the network interfaces, and focuses on the dynamic updating of the DPUs to support their utilization in dynamic environments, and on the utilization of authentication techniques to increase the level of security.
Journal Article

Multi-objective design space exploration of embedded systems

TL;DR: A Design Space Exploration (DSE) framework to simulate the target system and to dynamically profile the target applications and to reduce the overall exploration time by computing an approximated Pareto set of configurations with respect to the selected figures of merit.
Journal ArticleDOI

A Survey on Compiler Autotuning using Machine Learning

TL;DR: A recent survey as discussed by the authors summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations, and (2) phase-ordering of optimizations.