Micro : a Mixed Criticality Scheduling Approach for

il y a 2 semaines

Brest, France Lab-STICC, Université de Bretange Occidentale Temps plein

Réf **ABG-127354**
- Stage master 2 / Ingénieur- Durée 6 mois- Salaire net mensuel Standard French internship support (~600€/m)- 03/12/2024- Lab-STICC, Université de Bretange Occidentale- Lieu de travail- Brest Bretagne France- Champs scientifiques- Informatique
- Mots clés- real-time operating systems, scheduling analysis, convolutional neural network-
- 31/01/2025**Établissement recruteur**:
**Site web**:
The Lab-STICC is a research unit of the French national center for scientific research (CNRS) involving two universities (Université de Bretagne Occidentale, Brest; Université de Bretagne Sud, Lorient) and three graduate schools of engineering (IMT Atlantique, ENSTA Bretagne and ENIB). Under the title UMR 6285, it is attached to department INS2I and INSIS of the CNRS.

**Description**:
**Context**
- Implementation of an MCS scheduler in a real-time operating system (RTOS). The RTOS targeted in this project is Zephyr [1].
- Implementation of CNN processing in “anytime” mode on embedded platforms. The PoC is used to study and compare the MCS approach on the Zephyr RTOS with classical approaches such as embedded Linux

**Problem statement**
- Lack of implementation/support of a mixed-criticality scheduler in RTOS: Although there has been a significant theoretical research effort in this area, the only existing implementation is a plugin of the LitmusRT benchmark [4], which does not meet our requirement to build a real functional “proof of concept” on an embedded platform.

**Objective**
- (1) Provide an implementation of an MCS scheduler in the Zephyr RTOS. We plan to realize two components: MCS-lib and MCS-kernel
- MCS-lib: a set of libraries to facilitate the implementation of MCS tasks.
- MCS-kernel: integration of the MCS scheduler in the Zephyr kernel..
- UNet for image segmentation
- YOLO for object detection
- Raspberry PI 5 Model B (Quad-core ARM Cortex-A72 processor)

**Contact**
- Hai Nam TRAN (hai-nam.tran [AT] univ-brest.fr)

**References**
[2] Olaf Ronneberger, Philipp Fischer et Thomas Brox, « U-Net: Convolutional Networks for Biomedical Image Segmentation », Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Springer International Publishing,‎ 2015, p. 234-241
[3] Joseph Redmon, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[4] J. M. Calandrino, H. Leontyev, A. Block, U. C. Devi and J. H. Anderson, "LITMUS^RT : A Testbed for Empirically Comparing Real-Time Multiprocessor Schedulers," 2006 27th IEEE International Re-al-Time Systems Symposium (RTSS'06), Rio de Janeiro, Brazil, 2006, pp. 111-126, doi: 10.1109/RTSS.2006.27.
[5] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.

**Profil**:
**Prise de fonction**:

- Dès que possible

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