Mobile Agentic Systems
Overview
AI technologies have increasingly penetrated applications running on mobile/edge devices, e.g., mobile phones and smart cameras. Yet, deploying state-of-the-art AI models with billions of parameters on resource-constrained mobile-/edge-devices still faces critical challenges due to the stringent compute and memory requirements of running these models. We are investigating solutions to address this timely problem, mainly in two directions: (1) offloading intensive AI inference workloads to an edge or cloud platform where more computational resources are available, while handling the inherent network variability, (2) optimizing the computations of big AI models to fit them to the resources of mobile/edge devices by leveraging sparsity and locality.
People
Marvin IllianPostdoc
Hanzhang MaPhD (external)
Huzaifa Shaaban KabakiboPhD