Travailler à TIMA

Propositions de stages


SRAM-Based PUF with STM Controllers

Équipe : TIMA Laboratory - AMfoRS team - 46 avenue Félix Viallet - 38031 GRENOBLE Cedex

Date de début : 15/02/2020

Durée : 6 months

Profil : A Physically Unclonable Function (PUF) exploits intrinsic manufacturing variability introduced in a device during the fabrication process to generate a signature, unique to each single device. In SRAM-based PUFs, the signature is generating by reading the content of the memory after the power-up.
In this internship we want to set-up a hardware platform based on multiple STM32 microcontrollers, able to read-out the content of the SRAM embedded in all the controllers, and automatically store the signatures in an external database. The platform will have to automatically repeat the measurements at different time instants (i.e., the platform must be able to switch on and off the microcontrollers).
The goal of the internship is to store enough data from many devices in order to: (i) evaluate the quality of the PUF that ca be obtained from the STM microcontrollers; (ii) to evaluate their reliability (i.e., how often the signatures change in time).
Prerequisites: STM32/ARM programming for writing a small routine able to read the content of the memory and transfer the data through an identified channel (e.g., USB, ethernet, serial), C programming (for writing the “server” application able to receive the data from the identified channel, to store all data, to drive the power supply of the boards).

Applications: Please send your resume, application letter with two recommendations (including education director), first year master’s degree grades (mandatory) and second year grades (if possible) to cyberalps-contact@univ-grenoble-alpes.fr.

For more information on the internship, please contact [giorgio.di-natale@univ-grenoble-alpes.fr]

Personne à contacter : Giorgio DI NATALE (giorgio.di-natale@univ-grenoble-alpes.fr) / Elena Ioana VATAJELU (ioana.vatajelu@univ-grenoble-alpes.fr)

Rémunération: 512 € / month

Niveau: Master 2

 

 

Behavioral-Level Fault Modelling for Spiking Neural Networks

Équipe : TIMA Laboratory - AMfoRS team - 46 avenue Félix Viallet - 38031 GRENOBLE Cedex

Date de début : February 3rd, 2020

Durée : 6 months

Profil : The main objective of this internship is to analyze the possible SNN faults and provide a dictionary of fault models and a classification of faults by their severity.

The computing performance needed by emerging electronic applications (such as Internet-of-Things and Big Data analytics) is posing a serious challenge to current computer architectures and technologies, which are required to provide increasing computing power while withstanding severe constraints on size, energy consumption and reliability. Conventional Von-Neumann architectures and memories are not likely to fulfil all the needs of modern applications, due to inherent technological and conceptual limitations. Hence, in order to be at the forefront of the electronic industry in terms of design and manufacturing capabilities, it is essential to focus research and innovation efforts on the development of novel non-Von Neumann architectures enabled by emerging technology devices. In this context, the neuromorphic computing paradigm has a huge potential when it makes use of emerging NV technologies (STT-MRAM, memristors), however, reliable and testable HW designs enabling the neu romorphic computing are still missing.
This internship 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).

See complete information

Personne à contacter : Elena Ioana VATAJELU (ioana.vatajelu@univ-grenoble-alpes.fr)

Rémunération: 512 € / month

Niveau: Master 2 / Dernière année d'école d'ingénieur

 

 

Design and Evaluation of a Hardware-implemented Spiking Neural Network (SNN) with Spike-Timing-Dependent-Plasticity (STDP) learning

Équipe : TIMA Laboratory - AMfoRS team - 46 avenue Félix Viallet - 38031 GRENOBLE Cedex

Date de début : February 3rd, 2020

Durée : 6 months

Profil : The main objective of this internship is to design and evaluate a HW-implemented SNN using analog leaky-integrate and fire neurons and spintronic synapses.

Conventional Von-Neumann architectures and memories are not likely to fulfil all the needs of modern applications, due to inherent technological and conceptual limitations. Hence, in order to be at the forefront of the electronic industry in terms of design and manufacturing capabilities, it is essential to focus research and innovation efforts on the development of novel non-Von Neumann architectures enabled by emerging technology devices. In this context, the neuromorphic computing paradigm has a huge potential when it makes use of spintronic NV technologies.
This internship 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 implementations of bio-inspired neural networks (Spiking Neural Networks).

See complete information

Personne à contacter : Elena Ioana VATAJELU (ioana.vatajelu@univ-grenoble-alpes.fr)

Rémunération: 512 € / month

Niveau: Master 2 / Dernière année d'école d'ingénieur