Today, many autonomous and mobile applications are unable to achieve their full performance potential due to power and energy constraints, limiting the adoption of high-value, AI-driven solutions. As artificial intelligence continues to increase the functional complexity of embedded systems, computing workloads and power consumption have grown significantly—often approaching the energy requirements of the vehicle's propulsion system itself.

The EFICAS project addresses this challenge by developing a scalable, general-purpose software platform powered by energy-efficient artificial intelligence algorithms. The platform leverages multicore heterogeneous computing architectures to maximize AI performance while minimizing energy consumption across resource-constrained embedded systems.

EFICAS supports localized, distributed, and cloud-based computing environments, providing a flexible architecture that integrates with existing commercial solutions. The project also focuses on runtime resource allocation, intelligent task scheduling, and hybrid computing strategies to optimize system performance, energy efficiency, and workload distribution in real time.

By significantly improving the energy efficiency of AI applications, EFICAS aims to advance next-generation solutions in autonomous and mobile robotics, telecommunications, and industrial automation. The project is carried out by a consortium of 21 innovative organizations from four countries, bringing together leading expertise in artificial intelligence, embedded systems, and energy-efficient computing.