Research team develops reconfigurable photonic computing architecture for lifelong learning

Artificial intelligence (AI) tasks have become increasingly abundant and complex, fueled by large-scale datasets. With the plateau of Moore's law and end of Dennard scaling, energy consumption becomes a major barrier to more widespread applications of today's heavy electronic deep neural models, especially in terminal/edge systems.

applications architecture artificial artificial intelligence become computing consumption datasets edge electronic electronics & semiconductors energy intelligence law lifelong learning major photonic computing reconfigurable research research team scale scaling systems tasks team terminal