Artificial intelligence (AI) is becoming an essential part of everyday life, from smart sensors and mobile devices to autonomous vehicles and robotics. However, the computer hardware used today struggles to meet the growing demand for fast learning and decision-making while keeping energy consumption low. Current computers process and store information in separate units, which makes them inefficient for tasks that require rapid adaptation and real-time responses.
Inspired by the human brain, neuromorphic computing aims to overcome these limitations by developing new types of hardware that can process information and learn in a more brain-like and energy-efficient way. Instead of relying on complex software running on conventional processors, neuromorphic systems seek to embed learning and adaptability directly into the physical hardware. Despite its promise, this field still faces major challenges, particularly in identifying materials and device designs that can naturally reproduce the dynamic behaviour of biological neurons.
The SMART project addressed this challenge by exploring a new approach to brain-inspired hardware based on an emerging functional material, TbMnO₃. This material can exhibit a special electrical behaviour that enables devices to generate self-sustained electrical oscillations, similar to the firing activity of neurons in the brain. The overall objective of the project was to understand and exploit these properties to develop compact, low-power artificial neuron elements capable of synchronising with each other and supporting learning processes.
Specifically, the project aimed to investigate how these material properties can be controlled, how oscillatory behaviour can be stabilised and synchronised, and how such devices could serve as building blocks for future neuromorphic systems. By linking fundamental material research to practical computing concepts, SMART contributes to the development of more efficient and adaptive hardware for next-generation AI
The results of the SMART project contribute to long-term efforts to develop computing technologies that are faster, more energy-efficient and better suited to intelligent applications operating close to where data are generated, such as sensors and edge devices. By demonstrating how learning-related functions can be embedded directly into hardware, the project opens new pathways toward low-power AI systems with reduced environmental impact.
At a broader level, the project supports European priorities in digital innovation and advanced computing by strengthening knowledge in emerging materials and brain-inspired technologies. The findings are relevant for future applications in autonomous systems, smart electronics and intelligent sensing, and they contribute to Europe’s capacity to develop sustainable and competitive AI hardware technologies.
