With the increasing prevalence of edge devices and the exponential growth of deep learning applications, there is a pressing need for efficient algorithms and techniques that can be applied to resource-constrained devices. This master thesis presents a novel system that combines distributed computing and early exit strategies to enable deep learning on edge devices. A multi-threaded algorithm is proposed to flexibly manage the load on each device based on both communication and computational requirements. Two solutions are presented, addressing common needs: accuracy constraint and input rate constraint. The primary objective is to investigate the feasibility, performance, and flexibility of the proposed techniques in resource-constrained environments.