L2ONN leverages the unique properties of light, such as spatial sparsity and multi-spectrum parallelism, to enable continuous training of neural networks. Unlike existing photonic neural networks, L2ONN is designed based on the physical nature of the interaction of light with matter.
Experimental evaluations have shown that L2ONN has significant capacity and high energy efficiency compared to electronic neural networks. This architecture is capable of solving complex machine learning problems such as image classification, voice recognition, and medical diagnosis.
Scientists hope that the development of photonic architecture for AI training will accelerate the development of more powerful photonic computing and support advanced machine intelligence technologies.
Source: Ferra
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