Design of in-memory high-dimensional-computing system
Conventional von Neumann architecture faces many challenges in dealing with data-intensive artificial intelligence tasks efficiently due to huge amounts of data movement between physically separated data computing and storage units. Novel computing-in-memory (CIM) architecture implements data processing and storage in the same place, and thus can be much more energy-efficient than state-of-the-art von Neumann architecture. Compared with their counterparts, resistive random-access memory (RRAM)-based CIM systems could consume much less power and area when processing the same amount of data. This makes RRAM very attractive for both in-memory and neuromorphic computing applications.
In the field of machine learning, convolutional neural networks (CNN) are now widely used for artificial intelligence applications due to their significant performance. Nevertheless, for many tasks, machine learning requires large amounts of data and may be computationally very expensive and time consuming to train, with important issues (overfitting, exploding gradient and class imbalance). Among alternative brain-inspired computing paradigm, high-dimensional computing (HDC), based on random distributed representation, offers a promising way for learning tasks. Unlike conventional computing, HDC computes with (pseudo)-random hypervectors of D-dimension. This implies significant advantages: a simple algorithm with a well-defined set of arithmetic operations, with fast and single-pass learning that can benefit from a memory-centric architecture (highly energy-efficient and fast thanks to a high degree of parallelism).
Thermodynamic Modelling of Complex Oxides for Smart Sensors
The search for more efficient materials follows a pattern that has changed very little over the years, involving poorly automated phases of synthesis, characterization and measurement of functional properties. Although this pattern has proved its strength in creating knowledge bases, it remains ineffective because it is time-consuming and generally covers a reduced range of compositions. The project Hiway-2-mat (https://www.pepr-diadem.fr/projet/hiway-2-mat/) seeks to use high-throughput combinatorial approaches and develop autonomous configurations to explore the compositional spaces of complex oxide materials, with the aim of accelerating the discovery of materials for smart sensors. In this context, CALPHAD method is a valuable tool for materials exploration, as it can provide a number of useful insights into the role of oxidation state or oxygen partial pressure on phase stability, and on the degree of substitution of doping elements in an oxide matrix. The aim is to calculate phase diagrams of complex oxides based on available databases, either to better prepare combinatorial experiments, or to drive the autonomous robot on the fly, providing additional information for on-line characterization.
Your role will be to:
1)Perform thermodynamic simulations using CALPHAD method and Thermo-Calc Software to predict the stability range of a set of complex oxides (Ba/Ca/Sr)(Ti/Zr/Sn/Hf)O3 at different temperatures and oxygen partial pressures. In this step, the candidate will also perform a critical review of the thermodynamic data available in the literature.
2)Include key elements in the available database.
3)Develop a rapid screening method to search for the most promising compositions.
The candidate will work closely with the experimental platform development team to guide future trials and adapt the method to better meet the needs of large-scale production.