Many advanced artificial intelligence (AI) systems, including those used in surgical robotics and high-speed financial trading, rely on processing large streams of raw data to identify important features almost instantly. However, traditional digital processors are reaching their physical limits. These electronic systems struggle to deliver the speed and data capacity that next-generation, data-heavy applications demand, resulting in slower performance and higher latency.
Researchers believe the key to overcoming these limitations may come from using light instead of electricity. This emerging approach, known as optical computing, uses light to carry out complex computations with extraordinary speed. One of the most promising technologies in this field involves optical diffraction operators, thin, plate-like components that calculate as light travels through them.
These systems are highly energy-efficient and capable of handling multiple data streams simultaneously. Yet, achieving operating speeds above 10 GHz has proven difficult because it requires extremely stable, coherent light, which is challenging to maintain.
A team led by Professor Hongwei Chen at Tsinghua University, China, has now developed an innovative solution to this challenge. As detailed in Advanced Photonics Nexus, the researchers created an optical feature extraction engine (OFE2) designed to perform optical-based data analysis across a range of real-world applications.
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