| Course label : | Neuromoprhic Technologies for Spiking Neural Networks |
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| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PHILIPPE PERNOD |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_ETECH_S3_NTS - Neuromoprh Tech for Spiking N. |
Education team
Teachers : Mister PHILIPPE PERNOD
External contributors (business, research, secondary education): various temporary teachers
Summary
The objective is to teach students Neuromorphic Technologies for Impulse Neural Networks (ANN), the keystone of "third generation artificial intelligence". Brief program: 1) Bioinspired information processing: - Nerve impulse in the living (characteristics of the brain, neurons, coding, biological membrane) 2) Artificial Neural Networks (ANN): brief history, architectures, software/hardware approaches, plasticity, supervised/unsupervised learning 3) Pulse Neuron Networks (PNN) - hardware implementations - for 3rd generation AI: (i) interest (response to the energy challenge, for which applications), description of the neuromorphic technologies (NT) used - all CMOS or integrating synapses from nanoelectronics - (ii) use of the PNNs in the development of AI 4) Coupling of NNS with bioinspired artificial sensors (retina, cochlea) 5) Bioinspired computing for hybrid biology / technology applications for information processing
Educational goals
Objectives (in terms of know-how): The objective is to teach students Neuromorphic Technologies for Impulse Neural Networks (SNN), the keystone of "third generation artificial intelligence". The student will understand the basic building blocks (neurons, synapses) required for the deployment of SNNs, acquire a culture related to neuromorphic technologies all CMOS or co-integrating synapses from nanoelectronics: organic or non-organic, magneto-electric. The coupling with bioinspired artificial sensors (retina, cochlea, ...) will be addressed. An opening to more exploratory directions aiming at reproducing the principles of information processing observed in biological systems using emerging technologies will also be proposed. This approach is particularly interested in reproducing intelligent sensor networks, exploiting the properties of complex systems at the nanoscale (i.e. reservoir computing) and exploring the coupling of electronics and biology for information processing. Acquired skills (direct/indirect): Consolidate the scientific culture of Master 2 students concerning neuromorphic technologies dedicated to 3rd generation AI
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: -
Online resources
Simulation software for practical works
Pedagogy
Lectures & Tutorials : 16 Practical work: 12 Personnel work: 20
Sequencing / learning methods
| Number of hours - Lectures : | 16 |
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| Number of hours - Tutorial : | 0 |
| Number of hours - Practical work : | 12 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Basics in Signal / Information processing
Maximum number of registrants
Remarks
This specific course is operated by University of Lille within the framework of the co-accreditation of the master between Centrale Lille and University of Lille.