Seminar Details
2025-11-17 (13:00) : From Cloud to Edge: How to Make AI Inference Accurate, Fast and Resource-Aware ?
At Nyquist Maxwell a.164 room.
Organized by Computer Science and Engineering
Speaker :
Patient NTUMBA WA NTUMBA (CNAM Paris)
Abstract :
Edge computing is becoming a critical enabler for real-time Internet of Things (IoT) applications powered by artificial intelligence (AI). These applications often require accurate, low-latency and high-throughput inference while operating on resource-constrained edge servers. In this seminar, we present a holistic framework for optimising AI model provisioning at the edge. Our approach combines HProfiler, a data-driven model profiling tool that generates AI model variants tailored for accuracy-throughput trade-offs and specific edge resources, with AIForwarder, a reinforcement learning-based mechanism that dynamically manages model activation and request-to-model forwarding. By balancing accuracy, energy consumption, and request loss, our solution adapts to dynamic workloads and heterogeneous IoT demands.Short Bio: Patient Ntumba is a postdoctoral researcher in the RoC team at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. He obtained his PhD from Sorbonne University, conducting his doctoral research at the INRIA Paris research center in the MiMove team. His research interests focus on networks, distributed systems, and optimization. His current work centers on in-network distributed learning and Edge AI inference.