CCA-secure constructions for FHE
Fully Homomorphic Encryption (FHE) is a corpus of cryptographic techniques that allow to compute directly over encrypted data. Since its inception around 15 years ago, FHE has been the subject of a lot of research towards more efficiency and better practicality. From a security perspective, however, FHE still raises a number of questions and challenges. In particular, all the FHE used in practice, mainly BFV, BGV, CKKS and TFHE, achieve only CPA-security, which is sometimes referred to as security against passive adversaries.
Over the last few years, a number of works have investigated the security of FHE in the beyond-CPA regime with new security notions (CPAD, FuncCPA, vCCA, vCCAD, and others) being proposed and studied, leading to new attacks and constructions and, overall, a better understanding of FHE security in that regime.
With respect to CCA security, recent works (2024) have defined new security notions, which are stronger than CCA1 and shown to be achievable by both exact and approximate FHE schemes. Leveraging on these advances, the present thesis will aim to design practical FHE-style malleable schemes enforcing CCA security properties, at least for specific applications.
Water at the hydrophilic direct bonding interface
The microelectronics industry is making increasing use of hydrophilic direct bonding technology to produce innovative substrates and components. CEA LETI's teams have been leaders in this field for over 20 years, offering scientific and technological studies on the subject.
The key role of water at the bonding interface can be newly understood thanks to a characterization technique developed at CEA LETI. The aim of this thesis is to confirm or refute the physico-chemical mechanisms at play at the bonding interface, depending on the surface preparations and materials in contact.
A large part of this work will be carried out on our cleanroom tools. The characterization of surface hydration using this original technique will be complemented by standard characterizations such as adhesion and adherence energy measurements, FTIR-MIR and SIMS analyses, and X-ray reflectivity at ESRF.
Scalable NoC-based Programmable Cluster Architecture for future AI applications
Context
Artificial Intelligence (AI) has emerged as a major field impacting various sectors, including healthcare, automotive, robotics, and more. Hardware architectures must now meet increasingly demanding requirements in terms of computational power, low latency, and flexibility. Network-on-Chip (NoC) technology is a key enabler in addressing these challenges, providing efficient and scalable interconnections within multiprocessor systems. However, despite its benefits, designing NoCs poses significant challenges, particularly in optimizing latency, energy consumption, and scalability.
Programmable cluster architectures hold great promise for AI as they enable resource adaptation to meet the specific needs of deep learning algorithms and other compute-intensive AI applications. By combining the modularity of clusters with the advantages of NoCs, it becomes possible to design systems capable of handling ever-increasing AI workloads while ensuring maximum energy efficiency and flexibility.
Summary of the Thesis Topic
This PhD project aims to design a scalable, programmable cluster architecture based on a Network-on-Chip tailored for future AI applications. The primary objective will be to design and optimize a NoC architecture capable of meeting the high demands of AI applications in terms of intensive computing and efficient data transfer between processing clusters.
The research will focus on the following key areas:
1. NoC Architecture Design: Developing a scalable and programmable NoC to effectively connect various AI processing clusters.
2. Performance and Energy Efficiency Optimization: Defining mechanisms to optimize system latency and energy consumption based on the nature of AI workloads.
3. Cluster Flexibility and Programmability: Proposing a modular and programmable architecture that dynamically allocates resources based on the specific needs of each AI application.
4. Experimental Evaluation: Implementing and testing prototypes of the proposed architecture to validate its performance on real-world use cases, such as image classification, object detection, and real-time data processing.
The outcomes of this research may contribute to the development of cutting-edge embedded systems and AI solutions optimized for the next generation of AI applications and algorithms.
The work performed during this thesis will be presented at international conferences and scientific journals. Certain results may be patented.
Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. There is a wide variety of technologies available, including photonic qubits. The latter are robust against decoherence and are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing. For the large-scale deployment of those applications, it is necessary to have cheap, compact and scalable devices. To reach this goal, silicon photonics platform is attractive. It allows implementing key components such as generation, manipulation and detection of photonic qubits.
Solid-state photon generation may occur with different physical processes. Among those, the non-linear photon pair generation has several benefits, such as working at room temperature, the ability to generate heralded single photon, or entangled photon pairs…
You will work on multiplexed parametric photon pair sources in order to surpass the inherent limits of the physical process for generating photon pairs. This will include the development, the fabrication monitoring, and the characterization in the laboratory. In the goal of a full integration on chip, it is necessary to be able to filter effectively unwanted light, in order to keep only photons of interest.
Hardware-aware Optimizations for Efficient Generative AI with Mamba Networks
Generative AI has the potential to transform various industries. However, current state-of-the-art models like transformers face significant challenges in computational and memory efficiency, especially when deployed on resource-constrained hardware. This PhD research aims to address these limitations by optimizing Mamba networks for hardware-aware applications. Mamba networks offer a promising alternative by reducing the quadratic complexity of self-attention mechanisms through innovative architectural choices. By leveraging techniques such as sparse attention patterns and efficient parameter sharing, Mamba networks can generate high-quality data with significantly lower resource demands. The research will focus on implementing hardware-aware optimizations to enhance the efficiency of Mamba networks, making them suitable for real-time applications and edge devices. This includes optimizing training and inference times, as well as exploring potential hardware accelerations. The goal is to advance the practical deployment of generative AI in resource-constrained domains, contributing to its broader adoption and impact.
Exploration of unsupervised approaches for modeling the environment from RADAR data
Radar technologies have gained significant interest in recent years, particularly with the emergence of MIMO radars and "Imaging Radars 4D". This new generation of radar offers both opportunities and challenges for the development of perception algorithms. Traditional algorithms such as FFT, CFAR, and DOA are effective for detecting moving targets, but the generated point clouds are still too sparse for precise environment model. This is a critical issue for autonomous vehicles and robotics.
This thesis proposes to explore unsupervised Machine Learning techniques to improve environment model from radar data. The objective is to produce a richer model of the environment to enhance data density and scene description, while controlling computational costs for real-time computing. The thesis will address the question of which types of radar data are best suited as inputs for algorithms and for representing the environment. The candidate will need to explore non-supervised algorithmic solutions and seek computational optimizations to make these solutions compatible with real-time execution.
Ultimately, these solutions must be designed to be embedded as close as possible to the sensor, allowing them to be executed on constrained targets.
MOCVD growth of 2D ferroelectric In2Se3 films for high density, low consumption nonvolatile memories
Room temperature ferroelectric thin films are the key element of high density, low consumption nonvolatile memories. However, with the further miniaturization of the electronics devices beyond the Moore’s law, conventional ferroelectrics suffer great challenge arising from the critical thickness effect, where the ferroelectricity is unstable if the film thickness is reduced to nanometer or single atomic layer limit. Two-dimensional (2D) materials, thanks to their stable layered structure, saturate interfacial chemistry, weak interlayer couplings, and the benefit of preparing stable ultra-thin film at 2D limit, are promising for exploring 2D ferroelectricity and related device applications. So far, proof of concept demonstrating 2D ferroelectricity has predominantly utilized small flakes (less than a few hundred µm) mechanically exfoliated from a bulk crystal. In particular, atomically thin alpha (or gamma)-In2Se3 lamellar semiconductor preserves a ferroelectric character at 2D limit.
Given the imperative for wafer-scale electronics applications, there is a pressing need for large area growth of high quality 2D materials using bottom-up processes. The objective of this PhD project is to develop the growth of lamellar In2Se3 in its alpha or gamma phase crystal structures by chemical vapor phase epitaxy (MOCVD) on large silicon substrates (200 mm). The proof of concept of a ferroelectric memory cell will be performed by directly depositing a metal electrode on the surface of the 2D ferroelectric material without damaging it.
3D chemical analysis of downscaled ePCM devices for sub-18 nm technology nodes using STEM-EDX tomography and machine learning tools
The context of this PhD is the recent progress of Phase-Change Memory technology in the embedded applications (ePCM). The ultimate scaling of ePCM for sub-18nm nodes poses many challenges not only in fabrication, but also in the physico-chemical characterization of these devices. The aim of the project is to study the 3D chemical segregation/crystallization phenomena in new PCM alloys integrated into planar and vertical ePCM scaled devices, using electron tomography in STEM-EDX (and 4D-STEM) mode. Given the extreme downscaling and the complex geometry of the devices, the focus will be on optimizing experimental conditions and applying machine learning and deep learning techniques to improve the quality and reliability of the obtained 3D results. A correlation with the device electrical behavior will be carried out to better understand the phenomena behind failures after endurance and after data loss at high temperatures.
A probe-corrected Cold-FEG NeoARM TEM (60kV-200kV) will be used for the tomographic data acquisition. It is equipped with two large solid angle SSD detectors (JEOL Centurio), a CEOS Energy-Filtering and Imaging Device (CEFID) and a Timepix3 direct electron camera. The candidate will also have access to in-house Python codes as well as to the computing resources needed to carry out the spectral and tomographic data analysis.
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.
Securing Against Side-Channel Attacks by Combining Lightweight Software Countermeasures
Side-channel attacks, such as analyzing a processor's electrical consumption or electromagnetic emissions, allow for the recovery of sensitive information, including cryptographic keys. These attacks are particularly effective and pose a serious threat to the security of embedded systems.
This thesis focuses on combining low-impact software countermeasures to strengthen security against side-channel attacks, an idea that remains poorly explored in the current state of the art. The goal is to identify synergies and incompatibilities between these countermeasures to create more effective and lightweight solutions. In particular, low-entropy masking countermeasures will be considered.
These ideas will be applied on cryptography algorithm, with a particular focus on post-quantum cryptography algorithms.
The thesis aims to develop new ways to secure software, offering better trade-offs between security and performance than existing approaches.