Generation of inference tools based on MLIR techniques for gradient management and mixed precision on diverse hardware targets

IFP Energies Nouvelles offers an innovative PhD opportunity at the intersection of Artificial Intelligence (AI) and High-Performance Computing (HPC). This project aims to develop inference engines based on the MLIR (Multi-Level Intermediate Representation) infrastructure to optimize the performance of AI algorithms requiring precise gradient calculations, multi-precision management, and efficient execution on heterogeneous architectures (CPU, GPU, FPGA, ARM, RISC-V, SiPearl).
The PhD candidate will address key challenges such as optimizing non-linear computations with reduced latency, increased precision, and enhanced performance through multi-precision techniques. They will propose mechanisms to manage interoperability between AI models and numerical solvers in demanding industrial environments, such as subsoil modeling.
The candidate will join a multidisciplinary team and contribute to significant technological advancements in the energy industry. This project will fully leverage modern hardware architectures and optimize the compilation pipeline using MLIR.
 

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. 
 

Academic requirements    University Master degree in  Computer Science and Information System, Data sciences, Applied Mathematics   
Language requirements    English level B2 (CEFR)