The Future of Deep Learning: Photonics
As technology develops, new methods and tools for communication proliferate. Such a rapid increase brings with it high data transfers and transaction volumes. As data processing increases, more energy consumption, heat increase, and capacity limits emerge. Scientists are looking for ways to process and transport the high data needed, especially in studies with Artificial Intelligence (AI) and Deep Learning, subsets of Machine Learning (ML).
Photonics offers a solution to these problems in new generation powerful technologies.
What is Photonics?
Photonics, also called the science of light, describes the generation’s science and technology. Characteristics of light waves and photons; It can be used to discover the universe, treat diseases, and even solve crimes. We know the colors of the rainbow are the electromagnetic spectrum, which corresponds to a tiny part of the visible light waves. Photonics also studies the broader wavelengths of radio, X–rays, ultraviolet rays, and infrared rays.
The dual nature of light is known as wave and particle duality. In this way, the light shows continuous electromagnetic and photon characteristics. The science of light is mainly concentrated in the fields related to optics. The best–known are geometric optics, physical optics, and quantum optics. Photonics, on the other hand, is a subset of optics–related sciences.
When Did Photonics Appear?
In the 17th century, Sir Isaac Newton showed that white light is a combination of lights of other colors. At the beginning of the 20th century, Max Planck and then Albert Einstein proposed the theory that light consists of waves and particles. This theory created a pretty controversial environment in the scientific world. On the other hand, photonics was coined for the first time in the 1960s with the invention of the laser by Theodore Maiman.
Photonics later became more critical with fiber optic communication technologies in the 1980s. Today, almost no technology or science does not involve light.
Photonics technologies are increasing their importance in the modern age and future in many fields, including sensors, optical computers, next–generation lasers and LEDs, visible light communication (Li–Fi), water purification, and quantum information processing.
Why are Photonic Technologies Needed?
Photonics, the importance of which is increasing day by day in today’s and future technologies, creates a revolution in technology. Night vision cameras and devices and lasers have become indispensable in today’s defense industry, especially for UAV/USAV vision and firing systems. Infra–Red (IR) and Shorth Wave Infra–Red (SWIR) cameras are finding more and more uses in the defense industry every day. On the other hand, although people are not aware of it, devices containing Photonic technology often appear in our daily lives. LED devices are one of them. In addition, production devices, high data transfer systems, advanced sensors, and imaging technologies are also in the field of photonics.
As technology develops, the need for communication and information technologies also increases. Thanks to Photonics, traditional systems that heat up due to more data usage are becoming faster and more efficient. In addition, Photonics–based display technologies enable extraordinary films and photographs. In short, Photonics technology is at the heart of the digital world.
The most significant impacts of Photonics science emerge in communication and information technologies. Copper conductors, which have been preferred for data transfers since the past, are starting to leave their place to light today. Fiber optic conductors, which convert electrical signals into optical signals with light emitting or laser diodes, allow data transfer at high speeds. In addition, Photonic technologies, used in innovative display technologies such as Virtual Reality (VR) Systems in the IT industry, invite users to interact with a new environment, with Augmented Reality (AR) technologies entering the equation.
The Effect of Photonics on Deep Learning
With the inclusion of Photonics in computers in the IT industry, new generation technologies are shaping the future.
Computers are now used in many areas such as identification with visuals, diagnosis in the medical field, perception of languages and instant translation with sounds, complex computer game setups, and autonomous vehicles. New technologies that allow such applications are called Deep Learning or Artificial Intelligence (AI) neural networks. Deep Learning, a subset of Machine Learning (ML) within computer science, is developing further thanks to the increased data rate with light science.
The use of optical processors and photons instead of electrons in neural networks processes, especially for information systems designed according to Deep Learning, provides an advantage in high–speed data transfer that is needed. With the help of virtual networks created with software, digital computing devices can operate at extraordinary speeds.
Photonic sensor processors give more effective results in high–capacity neural networks than graphics processors (GPU), which are widely used today. With the new technique that George Washington University researchers are working on, they can increase the processor capacity up to three times by replacing electrical energy with light energy. In this way, it is possible to perform transactions that require high data transfer, such as Deep Learning and Artificial Intelligence (AI), more efficiently. Unfortunately, the increased data needed during deep understanding also causes excessive power consumption. Photonic systems, which can operate comfortably with low energy, also provide significant savings in this area.
Although modern computer hardware is well–suited for matrix operations, with the popularization of Deep Learning, it has begun to fall short of increasing data capacities. Matrix calculations related to deep understanding are digitized by reducing them to multiple multiplication and accumulation operations, where pairs of numbers are multiplied together and summed. Deep Learning needs these multiplication and addition operations more and the years. These processes create a severe environmental footprint due to their intense energy consumption. A 2019 study found that teaching a natural language operation powered by Deep Learning produces five times the carbon dioxide emissions a car has over its entire lifetime.
Although modern computers make a significant contribution to the development of deep understanding, the benefit of an alternative and more cost–effective approach to creating neural networks inevitably emerges.
Optical data communication offers a faster and low–energy solution, while the same features appear in optical computers.
An optical computer offers ten times faster processing capacity than models with electrical systems. In addition, while more space is needed with the cabling to be used in traditional computers, optical systems that can even pass over each other allow much smaller computers to be produced.
Optical information systems using Photonic technology promise to create a severe growth effect on Deep Learning in theory. Computer systems, neural networks, and software needed for this are being developed.
Luminous, affiliated with Princeton University, is working on a powerful new neural network project they call “Laser Nerve.” These neural networks mimic biological neural networks and operate at low energy, just like the human brain. The project, which is currently in the research phase, offers a massive opportunity for Deep Learning and Machine Learning (ML).
Click for more information on this topic;
