Despite the rapid advancements in optical computing, it is still far from replacing electronic chips that run neural networks outside of labs. The reported figures about photonic supremacy often don't tell the whole story, and lab systems need to be scaled up before they can show competitive advantages. Some researchers believe ONN-based AI systems will first find success in specialized applications where they provide unique advantages. The grand vision of an optical neural network surpassing electronic systems for general use remains a goal worth pursuing.
Key takeaways:
- Researchers have been developing optical neural networks (ONNs) that use light to perform computations, which can be faster and more efficient than traditional electronic devices.
- A new optical network called HITOP has been developed which aims to increase computation throughput with time, space, and wavelength, potentially overcoming some of the drawbacks of ONNs.
- Another team at the University of Pennsylvania has developed an ONN that offers unusual flexibility, with the ability to reconfigure the system's function by changing laser patterns.
- While ONNs have shown promise, they are still far from replacing electronic chips, and there are many engineering challenges to overcome before they can be competitive in general use. However, they may find success in specialized applications where they offer unique advantages.