CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees.
Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask for more runtime flexibility as well as a minimization of their overall environmental footprint.
For these applications, a novel adaptive runtime strategy is required that can optimize dynamically at runtime the deployment of software payloads on hardware nodes, with a mixed-critical objective that combines real-time guarantees with the minimization of the environmental footprint.
Theoretical design of quasi-atomic systems in the band gap of semiconductors/insulators for quantum application
The rise of room-temperature applications like single photon emission of the negatively charged nitrogen-vacancy NV center in diamond has renewed the interest in the search for materials having a quasi-atomic system QAS analogous to that of NV, mainly characterized by the presence of well localized in-gap defect levels generate occupied by electrons and leading to high spin states. In this Ph.D. work, theoretical methods will be used to design new QASs analogous to the NV center as well as, in selected QAS, to predict charge states and explore the effect of the proximity of the surface on the thermodynamic stability and on the spin state structure. The objectives are to design new QASs; To predict charge states of selected QASs in the bulk of the host material; To study changes in the charge state brought by the proximity of the surface; To extend the Hubbard model used to compute the excited states and to account for the electron-lattice interaction in the calculation of the excited states; To study the effect of the presence of deep level states in the band gap on the transport of electrons and phonons. The methodology developed at LSI to design new QASs with high spin states will be exploited and new systems analogous to the NV center will be looked for. Density functional theory (DFT) and a Hubbard model developed at LSI will be the main tools of this PhD.
In-Sensor Computing for MEMS Sensors: Toward an Electromechanical Neural Network
The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy consumption of systems. In this context, the concept of in-sensor computing has emerged, integrating data acquisition and processing within the sensor itself. By leveraging the physical properties of sensors and alternative computing paradigms, such as reservoir computing and neuromorphic computing, in-sensor computing eliminates the energy-intensive steps of signal conversion and processing.
Applying this concept to MEMS sensors enables the processing of signals such as acceleration, strain, or acoustic signals, with a significant reduction, or even elimination, of traditional electronic components. This has rekindled interest in mechanical computing devices and their integration into MEMS sensors like microphones and accelerometers. Recent research explores innovative MEMS devices integrating recurrent neural networks or reservoir computing, showing promising potential for energy efficiency. However, these advancements are still limited to proof-of-concept demonstrations for simple classification tasks with a very low number of neurons.
Building on our expertise in MEMS-based computing, this doctoral work aims to push these concepts further by developing a MEMS device that integrates a reprogrammable neural network with learning capabilities. The objective is to design an intelligent sensor that combines detection and preprocessing on a single chip, optimized to operate with extremely low energy consumption, in the femtoJoule range per activation. This thesis will focus on the design, fabrication, and validation of this new device, targeting low-frequency signal processing applications in high-temperature environments, paving the way for a new generation of intelligent and autonomous sensors.