Software support for sparse computation
The performance of computers has become limited by data movement in the fields of AI, HPC and embedded computing. Hardware accelerators do exist to handle data movement in an energy-efficient way, but there is no programming language that allows them to be implemented in the code supporting the calculations.
It's up to the programmer to explicitly configure DMAs and use function calls for data transfers and do program analysis to identify memory bottleneck
In addition, compilers were designed in the 80s, when memories worked at the same frequency as computing cores.
The aim of this thesis will be to integrate into a compiler the ability to perform optimizations based on data transfers.
EM Signature Modeling in Multi-path Scenario for Object Recognition and Semantic Radio SLAM
Context:
The vision for future communication networks includes providing highly accurate positioning and localization in both indoor and outdoor environments, alongside communication services (JCAS). With the widespread adoption of radar technologies, the concept of Simultaneous Localization and Mapping (SLAM) has recently been adapted for radiofrequency applications. Initial proof-of-concept demonstrations have been conducted in indoor environments, producing 2D maps based on mmWave/THz monostatic backscattered signals. These measurements enable the development of complex state models that detail the precise location, size, and orientation of target objects, as well as their electromagnetic properties and material composition.
Beyond simply reproducing maps, incorporating object recognition and positioning within the environment adds a semantic layer to these applications. While semantic SLAM has been explored with video-based technologies, its application to radiofrequency is still an emerging area of research. This approach requires precise electromagnetic models of object signatures and their interactions with the surrounding environment. Recent studies have developed iterative physical optics and equivalent current-based models to simulate the free-space multistatic signature of nearby objects.
PhD Thesis:
The objective of this thesis is to study and model object backscattering in a multi-path scenario for precise imaging and object recognition (including material properties). The work will involve developing a mathematical model for the backscattering of sensed objects in the environment, applying it to 3D SLAM, and achieving object recognition/classification. The model should capture both near- and far-field effects while accounting for the impact of the antenna on the overall radio channel. The study will support the joint design of antenna systems and the associated processing techniques (e.g., filtering and imaging) required for the application.
The PhD student will be hosted in the Antenna and Propagation Laboratory at CEA LETI in Grenoble, France. The research will be conducted in partnership with the University of Bologna.
Application:
The position is open to outstanding students with a Master of Science degree, “école d’ingénieur” diploma, or equivalent. The student should have a specialization in telecommunications, microwaves, and/or signal processing. The application must include a CV, cover letter, and academic transcripts for the last two years of study.
Advanced RF circuit design in a system and technology co-optimization approach
This thesis addresses the two major challenges facing Europe today in terms of integrating the communication systems of the future. The aim is to design RF integrated circuits using 22nm FDSOI technology in the frequency bands dedicated to 6G, which will not only increase data rates but also reduce the carbon footprint of telecoms networks. At the same time, it is essential to consider the evolution of silicon technologies that could improve the energy efficiency and effectiveness of these circuits. This work will be carried out with an eye to the design methodology of radio frequency systems.
Within the framework of the thesis, the objective will be broken down into three phases. Firstly, simulation tools will be developed to predict the performance of Leti's future 10nm FDSOI technology. The second stage will involve identifying the most relevant architectures available in the literature for the application areas envisaged for the technology. A link with upstream telecoms projects will be systematically established to ensure that the candidate understands the systems' challenges.
Finally, in order to validate the concepts developed, the design of an LNA and a VCO as part of an ongoing project in the laboratory will be proposed.
The candidate will join a large team that works on new communication systems and addresses aspects of architectural study, modeling and design of integrated circuits. The candidate must have serious skills in the design of integrated circuits and radio frequency systems as well as good ability to work in a team.
Scalable thermodynamic computing architectures
Large-scale optimisation problems are increasingly prevalent in industries such as finance, materials development, logistics and artificial intelligence. These algorithms are typically realised on hardware solutions comprising clusters of CPUs and GPUs. However, at scale, this can quickly translate into latencies, energies and financial costs that are not sustainable. Thermodynamic computing is a new computing paradigm in which analogue components are coupled together in a physical network. It promises extremely efficient implementations of algorithms such as simulated annealing, stochastic gradient descent and Markov chain Monte Carlo using the intrinsic physics of the system. However, no clear vision of how a realistic programmable and scalable thermodynamic computer exists. It is this ambitious challenge that will be addressed in this PhD topic. Aspects ranging from the development computing macroblocks, their partitioning and interfacing to a digital system to the adaptation and compilation of algorithms to thermodynamic hardware may be considered. Particular emphasis will be put on understanding the trade-offs required to maximise the scalability and programmability of thermodynamic computers on large-scale optimisation benchmarks and their comparison to implementations on conventional digital hardware.
Towards a multimodal photon irradiation platform: foundations and conceptualization
Photonic irradiation techniques exploit the interactions between a beam of high-energy photons and matter to carry out non-destructive measurements. By inducing photonuclear reactions such as photonic activation, nuclear resonance fluorescence (NRF) and photofission, these irradiation techniques enable deep probing of matter. Combining these different nuclear measurement techniques within a single irradiation platform would enable precise, quantitative identification of a wide variety of elements, probing the volume of the materials or objects under study. The high-energy photon beam is generally produced by the Bremsstrahlung phenomenon within a conversion target of a linear electron accelerator. An innovative alternative is to exploit the high-energy electrons delivered by a laser-plasma source, converted by Bremsstrahlung radiation or inverse Compton scattering. A platform based on such a source would open up new possibilities, as laser-plasma sources can reach significantly higher energies, enabling access to new advanced imaging techniques and applications. The aim of this thesis is to establish the foundations and conceptualize a multimodal photonic irradiation platform. Such a device would aim to be based on a laser-plasma source and would enable the combination of photonic activation, nuclear resonance fluorescence (NRF) and photofission techniques. By pushing back the limits of non-destructive nuclear measurements, this platform would offer innovative solutions to major challenges in strategic sectors such as security and border control, radioactive waste package management, and the recycling industry.
Multipath-based Cooperative Simultaneous Localization & Mapping through Machine Learning
The goal of this PhD is to explore the potential of machine learning (ML) tools for simultaneous localization and mapping (SLAM) applications, while leveraging multipath radio signals between cooperative wireless devices. The idea is to identify characteristic features of the propagation channels observed over multiple radio links, so as to jointly determine the relative positions of the mobile radio devices, as well as those of scattering objects present in their vicinity. Such radio features typically rely on the arrival times of multipath echos of the transmitted signals. The envisaged approach is expected to benefit from multipath correlation as the radio devices are moving, as well as from spatial diversity and information redundancy through multi-device cooperation. The developed solution will be evaluated on both real measurements collected with integrated Ultra Wideband devices in a reference indoor environment, and synthetic data generated with a Ray-Tracing simulator. Possible applications of this research concern group navigation in complex and/or unknown environments (incl. fleets of drones or robots, firefighters…).
HW/SW Contracts for Security Analysis Against Fault Injection Attacks on Open-source Processors
This thesis focuses on the cybersecurity of embedded systems, particularly the vulnerability of processors and programs to fault injection attacks. These attacks disrupt the normal functioning of systems, allowing attackers to exploit weaknesses to access sensitive information. Although formal methods have been developed to analyze the robustness of systems, they often limit their analyses to hardware or software separately, overlooking the interaction between the two.
The proposed work aims to formalize hardware/software (HW/SW) contracts specifically for security analysis against fault injection. Building on a hardware partitioning approach, this research seeks to mitigate scalability issues related to the complexity of microarchitecture models. Expected outcomes include the development of techniques and tools for effective security verification of embedded systems, as well as the creation of contracts that facilitate the assessment of compliance for both hardware and software implementations. This approach could also reduce the time-to-market for secure systems.
New machine learning methods applied to side-channel attacks
Products secured by embedded cryptographic mechanisms may be vulnerable to side-channel attacks. Such attacks are based on the observation of some physique quantities measured during the device activity, whose variation may provoke information leakage and lead to a security flaw.
Today, such attacks are improved, even in presence of specific countermeasures, by deep learning based methods.
The goal of this thesis is go get familiarity with semi-supervised and self-supervised Learning state-of-the-art and adapt promising methods to the context of the side-channel attacks, in order to improve performances of the attacks in very complex scenarios. A particular attention will be given to attacks against secure implementations of post-quantum cryptographic algorithms.
Cryptographic security of RISC-V processor enclaves with CHERI
CHERI (Capability Hardware Enhanced RISC Instructions) is a solution for securing the processor against spatial and temporal memory leaks by transforming any pointer into a capability that clearly defines the access limits to the data or instructions addressed.
In this thesis, we propose to enrich CHERI and its control-flow integrity capabilities on a RISC-V application processor, by protecting instructions right up to their execution against any type of modification. Secondly, based on authenticated memory encryption, we will study the possibility of using CHERI to define secure enclaves enabling cryptographic isolation between processes. The processor will be modified so that each process is encrypted with its own key and can have a secure life cycle. All keys must be efficiently protected in hardware.
Contact : olivier.savry@cea.fr
Topologic optimization of µLED's optical performance
The performance of micro-LEDs (µLEDs) is crucial for micro-displays, a field of expertise at the LITE laboratory within CEA-LETI. However, simulating these components is complex and computationally expensive due to the incoherent nature of light sources and the involved geometries. This limits the ability to effectively explore multi-parameter design spaces.
This thesis proposes to develop an innovative finite element method to accelerate simulations and enable the use of topological optimization. The goal is to produce non-intuitive designs that maximize performance while respecting industrial constraints.
The work is divided into two phases:
Develop a fast and reliable simulation method by incorporating appropriate physical approximations for incoherent sources and significantly reducing computation times.
Design a robust topological optimization framework that includes fabrication constraints to generate immediately realizable designs.
The expected results include optimized designs for micro-displays with enhanced performance and a methodology that can be applied to other photonic devices.