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Propositions de thèses


Test and Reliability of Emerging Memory-based Spiking Neural Networks

Équipe : AMfoRS

Date de début : 01/01/2020

Durée : 36 months

Profil : This PhD thesis is concerned with the following research areas: (i) emerging memory technologies (memristors and/or spintronic devices) used in a non-Von Neumann context, (ii) hardware dependability (robustness, reliability and test) and design-for-dependability, (iii) hardware implementations of bio-inspired neural networks (Spiking Neural Networks). Research hypothesis: the strong restrictions on the size of embedded Spiking Neural Network architectures (limited silicon area and interconnectivity ability) require minimization of the network redundancy which in turn reduces its the intrinsic fault tolerance. There is an acute need to evaluate the reliability and perform manufacturing test of the neuromorphic hardware architectures to guarantee their correct operation and robustness.

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Personne à contacter : Elena Ioana VATAJELU

 

 

Extreme Learning Machine for embedded neural networks (MIAI – Joint industry/academy PhD grant)

Équipe : CDSI / ST Imaging

Date de début : 01/11/2019

Durée : 3 years

Profil : PROJECT TITLE: MIAI @ Grenoble Alpes
SUBJECT TITLE: Extreme Learning Machine for embedded neural networks
RESEARCH FIELD (cf mots clefs sur Euraxess Jobs): computer vision, mathematics, embedded architecture
SCIENTIFIC DEPARTMENT (LABORATORY’S NAME): STMicroelectronics – Imaging / TIMA Laboratory
DOCTORAL SCHOOL’S: MST2I, Mathématiques, Sciences et technologies de l’Information Informatique
SUPERVISOR’S NAME: Marina NICOLAS (ST), Stéphane MANCINI (HDR, TIMA)

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Personne à contacter : Marina NICOLAS (ST) / Stéphane MANCINI (HDR, TIMA Lab)

 

 

Exploration of security threats in In-Memory Computing Paradigms

Équipe : AMfoRS

Date de début : September 2019

Durée : 3 years

Profil : This PhD tipic is related to the security aspects to be considered for an In-Memory computing implementation. Indeed, the tight coupled memory-computing elements vulnerabilities have to be analyzed in hybrid NV-CMOS technologies. This research direction is very ambitious and can lead to consequent research direction.

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Personne à contacter : Giorgio DI NATALE / Lorena ANGHEL / Elena-Ioana VATAJELU