Sponsors & Partners
The exponential growth in the size and complexity of Artificial Intelligence models, particularly Deep Neural Networks and Large Language Models, has led to unsustainable computational and energy demands. This track aims to bridge the gap between algorithmic innovation and hardware efficiency by exploring Energy-Efficient AI and Neuromorphic Computing. Neuromorphic engineering draws inspiration from the biological brain to design highly efficient, event-driven architectures (such as Spiking Neural Networks) and the newly-designed neuromorphic hardware. This track will provide a dedicated forum for researchers to present cutting-edge solutions in low-power edge AI and brain-inspired computational models, paving the way for sustainable AI systems.