What Neural Networks do?
The foundation of machine learning and artificial intelligence systems that can perceive the environment and adapt their own behaviour by analysing the effects of prior actions and working autonomously is now provided by neural networks, distributed computing structures that are inspired by the structure of a biological brain. They are applied in numerous fields, including bioinformatics, genetic and molecular sequencing, high-performance computing, voice and image recognition and synthesis, autonomous vehicles, augmented reality, and systems.
Why Photonic circuits are believable?
For neural networks, photonic circuits hold great promise because they enable the construction of computer components that consume little energy. The Politecnico di Milano has been developing programmable photonic processors built on silicon microchips of a few mm2 in size for use in the field of data transmission and processing, and these gadgets are now being used to create photonic neural networks.
How it works?
The chip has a photonic accelerator that uses a configurable grid of silicon interferometers to carry out calculations swiftly and effectively. Less than a billionth of a second (0.1 nanoseconds) is the calculation time, which is equal to the transit period of light in a chip a few millimetres in size.
Although the benefits of photonic neural networks are well known, network training was one of the key components that was needed to fully realise their potential. It’s comparable to owning a sophisticated calculator but not knowing how to use it. implementing training methods for photonic neurons that are comparable to those of traditional neural networks. The photonic “brain” is capable of precision that is comparable to that of a traditional neural network, but faster and with a significant reduction in energy consumption. These are all components that make up applications for quantum computing and artificial intelligence.