Recent research has made significant strides in the development of optical neural networks, presenting a sustainable alternative to the energy and resource-intensive models currently in use. By leveraging light propagation through multimode fibers and a minimal number of programmable parameters, researchers have achieved comparable accuracy to traditional digital systems with significantly reduced memory and energy requirements. This innovative approach offers a promising pathway toward energy-efficient and highly efficient artificial intelligence hardware solutions.

A novel architecture for optical neural networks utilizes wavefront shaping to precisely manipulate the travel of ultrashort pulses through multimode fibers, enabling nonlinear optical computation.

Present-day artificial intelligence systems rely on billions of adjustable parameters to accomplish complex objectives. Yet, the vast quantity of these parameters incurs significant expenses. The training and implementation of such extensive models demand considerable memory and processing power, available only in enormous data center facilities, consuming energy on par with the electrical demands of medium-sized cities. In response, researchers are currently reevaluating both the computing infrastructure and the