ED Mathématiques et Informatique
Circuit partitioning for multi-FPGA platforms
by Julien RODRIGUEZ (LaBRI - Laboratoire Bordelais de Recherche en Informatique)
The defense will take place at 9h00 - Salle Ada Lovelace 200, avenue de la Vieille Tour 33405 Talence
in front of the jury composed of
- François PELLEGRINI - Professeur des universités - Université de Bordeaux - Directeur de these
- Cevdet AYKANAT - Professeur - Université de Bilkent - Rapporteur
- Dirk STROOBANDT - Professeur - Université de Gand - Rapporteur
- Lélia BLIN - Professeure - Université Paris-Cité - Examinateur
- Viet Hung NGUYEN - Professeur - Université de Clermont-Auvergne - Examinateur
An FPGA ('Field Programmable Gate Array') is an integrated circuit comprising a large number of programmable and interconnectable logic resources, which allow one to implement, by programming, a digital electronic circuit such as a microprocessor, a compute accelerator or a complex hybrid system-on-chip. FPGAs are widely used in the field of integrated circuits design, because they allow one to obtain prototype circuits very quickly, without having to manufacture the chip on silicon. However, some circuits are too big to be implemented on a single FPGA. To address this issue, it is possible to use a platform consisting of several highly interconnected FPGAs, which can be seen as a single virtual FPGA giving access to all the resources of the platform. This solution, although elegant, poses several problems. In particular, the existing tools do not account for all the constraints of the placement problem to be solved in order to efficiently map a circuit onto a multi-FPGA platform. For example, current cost functions are not designed to minimize signal propagation times between FPGA registers, nor do they take into account the capacity constraints induced by the routing of connections. The aim of this PhD work is to design hypergraph partitioning and placement models adapted to the problem of circuit layout on a multi-FPGA platform. These models will be specifically designed to meet the objectives and performance criteria defined by circuit designers.
Quantification and characterization of autoimmune and allergic diseases using deep learning methods
by Guillaume MARTINROCHE (IMB - Institut de Mathématiques de Bordeaux)
The defense will take place at 14h00 - Salle de conférence Institut de Mathématiques de Bordeaux UMR 5251 351, cours de la Libération 33405 Talence
in front of the jury composed of
- Olivier SAUT - Directeur de recherche - Université de Bordeaux - Directeur de these
- Luciana TANNO KASE - Professeure des universités - praticienne hospitalière - Centre Hospitalier Universitaire de Montpellier - Rapporteur
- Soleakhena KEN - Ingénieure de recherche - IUCT-Oncopole, CRCT (Equipe RADOPT) - Rapporteur
- Emanuele RATTI - Maître de conférences - University of Bristol - Examinateur
- Nicolas PAPADAKIS - Directeur de recherche - Université de Bordeaux - Examinateur
Diagnostic assistance tools based on artificial intelligence (AI) and capable of integrating several types of data, will be crucial in the next coming years in helping practitioners provide more personalized, precision medicine for patients. Autoimmune and allergic diseases are perfect examples of complex, multi-parametric diagnostics that could benefit from such tools. Antinuclear antibodies (ANA) on human epithelial cells (HEp-2) are important biomarkers for the screening and diagnosis of autoimmune diseases. For harmonization of biological practices and clinical management, automatic reading and classification of ANA immunofluorescence patterns for HEp-2 images according to the nomenclature recommended by the International Consensus on Antinuclear Antibody Patterns (ICAP) seems to be a growing requirement. In our study, an automatic classification system for 26 IIF patterns of HEp-2 cells images was developed using a supervised learning methodology, based on a complete collection of HEp-2 cell images from Bordeaux University Hospital labelled accordingly to ICAP recommendations and local practices. The system consists of a classifier for nucleus patterns only (16 patterns and allowing recognition of up to two aspects per image) and a second classifier for cytoplasm aspects only (8 patterns). On the strength of promising results, the proposed system should contribute to the automatic recognition of ANA patterns in medical biology laboratories, enabling reflex quantitative tests targeted on a few autoantibodies, ultimately facilitating efficient and accurate diagnosis of autoimmune diseases. Allergen microarrays, enable the simultaneous detection of up to 300 specific IgE antibodies and are part of a bottom-up diagnostic approach in which, on the basis of the broadest possible analysis, we then seek to determine which allergens are likely to explain the patient's symptoms. However, the mass of data produced by this single analysis is beyond the analytical capacity of the average user and the large number of results obtained simultaneously can mask those that are truly clinically relevant. A database of 4271 patients (Société Française d'Allergologie) was created, including allergen microarrays data and twenty-five demographic and clinical data. This database allowed the development of the first models capable of predicting patients' allergic profiles thanks to an international data challenge. The best F1-scores were around 80%. A more comprehensive tool adapted to daily practice is currently under development. Based essentially on microarrays data and a very few clinical and demographic data, it will be able to provide clinicians with a probability of molecular allergy by protein family, thus limiting diagnostic delays and the need for oral provocation tests. Diagnostic aids using so-called AI technologies are helping to improve the efficiency of current techniques, freeing up time for repetitive, low-value-added tasks. These tools are generally poorly perceived by practitioners, who feel that they are losing their expertise, and even that they are being replaced by algorithms. This impression is particularly strong in Medical Biology, where this improvement directly affects the function of the Medical Biologist. In an attempt to better understand this, we took a closer look at the relationship of trust, if there can be one, between the practitioner and the diagnostic tool. Thanks to a survey of medical biologists working on the analysis of aspects of HEp-2 cells, a certain reticence can be highlighted, with reasons linked to performance and unfamiliarity with the systems. The deployment and mass adoption of similar strategies in the field of biological hematology shows real interest once performance has been established. The development of two diagnostic assisting tools for autoimmune and allergic diseases is laying the foundations for improved results and lasting integration into a more personalized, precision medicine.