Phd Position F/m Memory Minimization for Neural Networks

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Niveau de diplôme exigé**: Bac + 5 ou équivalent

**Fonction**: Doctorant

**Niveau d'expérience souhaité**: Jeune diplômé

**A propos du centre ou de la direction fonctionnelle**:
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area.

The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

**Mission confiée**:
**Context on memory peak minimization**
- **scheduling**: to schedule tasks according to their impact on memory (for example, tasks consuming data and decreasing the amount of live memory should be executed as soon as possible);
- **offloading**: to move live data to a slower but larger upper-level memory (e.g., cache, RAM, disk), and reloading them when required, which gives the opportunity to execute another data intensive task in between;
- **recomputing (aka rematerialization)**: to erase and recompute data, by re-executing the tasks having produced more data than they consumed, and keeping their (smaller) input data live instead of the (larger) output one, which also gives the opportunity to execute another data intensive task in between.

Considering all three techniques, scheduling, offloading, and recomputing, gives rise to trade-offs between the minimization of the memory peak and the execution time; this problem is challenging and PSPACE-complete.

**Principales activités**:
**Description and objectives of the PhD**

During the previous years, we have addressed the memory peak minimization problem of general task graphs by using only the scheduling technique [1, 2]. Our approach, based on original graph transformations, finds the optimal sequential schedule in terms of memory peak for a wide class of task graphs. This technique is able to optimally solve the problem on some large dataflow task
graphs, up to 50, 000 tasks in our experiments.

Offloading consists of data movement from a size-limited memory (RAM or GPU global memory) to a bigger but slower one (disk or RAM, respectively). Recomputing is useful for the training phase of neural networks, decomposed in two passes: forward and back propagation. It can be used to store only a part of all neurons’ outputs during the forward pass, so that the missing ones will
be recomputed during the back propagation pass.

The overall objective of the PhD is, by taking into account the specificities of a given neural network and by using the three techniques mentioned above, to minimize the execution time overhead while fitting in a given memory budget (i.e., optimization under constraint). This represents an opposite viewpoint compared to our previous work were the memory peak was minimized for a
constant time budget.

**Compétences**:
and compilation. Good relational and English skills are also important for the project

**Avantages**:

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours (90 days per year)
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

**Informations générales**:

- **Thème/Domaine**: Systèmes embarqués et temps réel
Systèmes d'information (BAP E)
- **Ville**: Montbonnot
- **Centre Inria**: Centre Inria de l'Université Grenoble Alpes
- **Date de prise de fonction souhaitée**: 2025-10-01
- **Durée de contrat**: 3 ans
- **Date limite pour postuler**: 2025-09-30

**Consignes pour postuler**:
**Sécurité défense**:
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

**Politique de recrutement**:
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes e