PHD: Compact Neural Network topology learning for on-sensor inference M/F
Internship Grenoble (Isère) Teaching
Job description
General information
Reference
2019-3034
Job level
30 - Graduate Entry Level
Position description
Posting title
PHD: Compact Neural Network topology learning for on-sensor inference M/F
Regular/Temporary
Temporary
Job description
You will PhD student withing the Imaging division of STMicroelectronics in Grenoble with close collaboration with CEA-Leti, in the context of the Grenoble Multidisciplinary Institute in Artificial Intelligence (MIAI). Your work will be part of a collaborative project between Gipsa-Lab, Tima-Lab, ST and CEA-Leti, called “Near-Sensor Neural Computing”.
Current developments on image sensors tend to imply embedded computer vision in order to upgrade imager capabilities, in particular to enable machine learning based inference. In the case of embedded imaging systems, decision-making now becomes the core device feature. However, such a system still remains limited due to its computational and memory load, embedded algorithm complexity, dynamic power consumption and leakage. In that context, major challenges thus lie in power consumption and silicon footprint, which both rely on data bandwidth (i.e., signal dimensions and quantization levels).
Taking into account all the aforementioned issues, the objective of this PhD will be to define strategies for automatic selection -or learning [6]- of the best network topology under identified hardware constraints (memory requirements, complexity in terms of number of clock cycles, silicon surface and energy).
Profile
Education Level Required - BAC+5 (INGENIEUR,DESS,DEA...)
Desired Competencies are: Neural Network, Computer Visson
Others skills: python, data analysis
Position localisation
Job location
Europe, France, Grenoble
Candidate criteria
Education level required
5 - Master degree
Experience level required
Less than 2 years
Languages
English (2- Business fluent)
Requester
Desired start date
01/11/2019