Lightweight CNN and Causal GNN for scene understanding
Scene understanding is a major challenge in computer vision, with recent approaches dominated by transformers (ViT, LLM, MLLM), which offer high performance but at a significant computational cost. This thesis proposes an innovative alternative combining lightweight convolutional neural networks (Lightweight CNN) and causal graph neural networks (Causal GNN) for efficient spatio-temporal analysis while optimizing computational resources. Lightweight CNNs enable high-performance extraction of visual features, while causal GNNs model dynamic relationships between objects in a scene graph, addressing challenges in object detection and relationship prediction in complex environments. Unlike current transformer-based models, this approach aims to reduce computational complexity while maintaining competitive accuracy, with potential applications in embedded vision and real-time systems.
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.
Implementation of TFHE on RISC-V based embedded systems
Fully Homomorphic Encryption (FHE) is a technology that allows computations to be performed directly on encrypted data, meaning that we can process information without ever knowing its actual content. For example, it could enable online searches where the server never sees what you are looking for, or AI inference tasks on private data that remain fully confidential. Despite its potential, current FHE implementations remain computationally intensive and require substantial processing power, typically relying on high-end CPUs or GPUs with significant energy consumption. In particular, the bootstrapping operation represents a major performance bottleneck that prevents large-scale adoption. Existing CPU-based FHE implementations can take over 20 seconds on standard x86 architectures, while custom ASIC solutions, although faster, are prohibitively expensive, often exceeding 150 mm² in silicon area. This PhD project aims to accelerate the TFHE scheme, a more lightweight and efficient variant of FHE. The objective is to design and prototype innovative implementations of TFHE on RISC-V–based systems, targeting a significant reduction in bootstrapping latency. The research will explore synergies between hardware acceleration techniques developed for post-quantum cryptography and those applicable to TFHE, as well as tightly coupled acceleration approaches between RISC-V cores and dedicated accelerators. Finally, the project will investigate the potential for integrating a fully homomorphic computation domain directly within the processor’s instruction set architecture (ISA).