PhD position - Embedded autonomous incremental learning

OPPORTUNITY DETAILS

Total reward

0 $

State University

CEA Tech

Area

Western Europe

Host Country

France

Deadline

31 Oct 2021

Study level

Doctoral Degree

Opportunity type

PhD

Specialities

Information technology and computer science

Opportunity funding

Not funding

Eligible Countries

This opportunity is destined for all countries

Eligible Region

All Regions

SL-DRT-21-0465

RESEARCH FIELD

Artificial intelligence & Data intelligence

ABSTRACT

The recent development of incremental learning algorithms for deep neural networks is an opportunity to imagine new intelligent sensor applications deployed in real environments. By being incrementally able to learn new tasks, the sensor will be able to personalize its behavior to its specific deployment milieu, allowing it to adapt to slow variations of its targeted tasks (e.g. the detection of different types of anomalies) or learn new tasks that were not initially anticipated. This possibility would make the service rendered by the autonomous sensor more and more relevant. The objective of this thesis is the exploration of the means by which the intelligent sensor can become fully autonomous in its evolution while taking into account the limited processing capability of the embedded system. Also, seeing the limited power consumption of the platform, the idea is to associate two embedded systems, a first which is “Always-on” and executes the nominal task of the application (e.g. the detection of different classes of events or anomalies), and a second which is “On-demand” which would be executed now and then, in order to retrain the model of the “Always-on” part. For coherence, it is necessary that the power consumption ratio of the two platforms be in a ratio of 1:100 to 1:1000 approximately.The challenges facing the design of such a system are many : The first is the design of detection mechanisms able to find false negative examples (slowly changing classes) as well as novel examples (new classes). These mechanisms must be executed on the “Always-on” platform, with the associated implementation constraints. A second difficulty concerns the retraining phase which is executed on the “On-demand” platform. This phase must take into account the structure of the “Always-on” model in order to be able to retrain it with new examples. This both in order to slowly learn the modifications of the existing detection task or to learn a new task without forgetting the old ones. Since this a new application space, the PhD candidate must be able to have a wide understanding of the subject and will necessarily have to address a wide number of domains including different incremental leaning algorithms, different deep learning training algorithms, and the hardware requirements necessary for running these algorithms in the embedded context.