Applications of photonics for artificial intelligence and neuromorphic computing

Photonics, the science of generating, controlling, and detecting light, is playing a paramount role in the future of Artificial Intelligence and Neuromorphic Computing.

Just in the same way that the 20th century depended on the electron to witness advances in electronics and electricity, the 21st century relies on the photon to propel many scientific breakthroughs in different fields and industries.

The science of photonics plays an important role driving innovation in an increasing number of fields. The application of photonics spreads across several sectors: From optical data communications to imaging, from lighting to displays, from manufacturing to life sciences, health care, security, and safety.

Some may think that it is extremely unlikely that Photonic Integrated Circuits (PICs) will completely replace electronic circuits. However, photonics is finding more applications in just about every industry including space science.

Scientists have given a fascinating new insight into the next steps to develop fast, energy-efficient, future computing systems that use light instead of electrons to process and store information — incorporating hardware inspired directly by the functioning of the human brain.

Neuromorphic Computing (Neuromorphic Engineering)

Neuromorphic computing is the result of inspiration taken from the human brain’s architecture and dynamics.

Neuromorphic Computing creates energy-efficient hardware for information processing, making it capable of highly sophisticated tasks.

Neuromorphic computing includes the production and use of artificial neural networks. Taking inspiration from the human brain, its goal is to design computer chips that are able to merge memory and processing.

The process simulates the human brain, where synapses provide a direct memory access to the neurons that process information.

Neuromorphic systems attempt to imitate how the human nervous system operates. This field of engineering tries to imitate the structure of biological sensing and information processing nervous systems.

In other words, neuromorphic computing implements aspects of biological neural networks as analogue or digital copies on electronic circuits.

Future potential for computer systems by using photonics in place of conventional electronics

A team of scientists, led by Professor C. David Wright, Head of Nano-Engineering Science and Technology Group, at the University of Exeter in the United Kingdom, has explored the future potential for computer systems by using photonics in place of conventional electronics.

The research paper, Photonics for Artificial Intelligence and Neuromorphic Computing, published in the journal Nature Photonics, focuses on potential solutions to one of the world’s most pressing computing problems — how to develop computing technologies to process data in a fast and energy efficient way.

Contemporary computers are based on the von Neumann architecture in which the fast Central Processing Unit (CPU) is physically separated from the much slower programme and data memory.

According to the researchers, this means computing speed is limited and power is wasted by the need to continuously transfer data to and from the memory and processor over bandwidth-limited and energy-inefficient electrical interconnects — known as the von Neumann bottleneck.

As a result, it has been estimated that more than 50 percent of the power of modern computing systems is wasted simply in this moving around of data.

“Clearly, a new approach is needed — one that can fuse together the core information processing tasks of computing and memory, one that can incorporate directly in hardware the ability to learn, adapt, and evolve, and one that does away with energy-sapping and speed-limiting electrical interconnects,” says Professor C. David Wright, from the University of Exeter’s Department of Engineering, who is one of the co-authors of the study.

Photonics Neuromorphic Computing

According to the study, Photonics Neuromorphic Computing is one such approach where signals are communicated and processed using light rather than electrons, thus, giving access to much higher bandwidths (processor speeds) and vastly reducing energy losses.

Moreover, the researchers try to make the computing hardware itself isomorphic with biological processing system (brains), by developing devices to directly mimic the basic functions of human brain neurons and synapses, then connecting these together in networks that can offer fast, parallelised, adaptive processing for Artificial Intelligence (AI) and Machine Learning (ML) applications.

The state-of-the-art of such photonics brain-like-computing and its likely future development, is the focus of the Photonics for Artificial Intelligence and Neuromorphic Computing research paper by a leading international team of researchers from The United States, Germany, and The United Kingdom.

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About the Author: Susan Fourtané is a freelance Science and Technology Journalist, a professional writer with 18 years experience writing and reporting for global media and industry publications. She’s a member of the ABSW, WFSJ, Society of Authors, and London Press Club.