All optical neural networks

Machine learning (ML) techniques have seen unprecedented success in a range of fields in recent years and, accordingly, significant investment from academia and industry alike. Today, large data centres train ML models on enormous volumes of data to achieve marginal gains in select, lucrative fields, from financial forecasting to autonomous vehicles. In academia, ML has provided a new toolbox for solving otherwise challenging problems and opened new avenues in all areas of science and technology research.

The success of such methods relies on scale - large volumes of data, computing power and processing time are used to solve even modest problems. Energy consumption is significant, with individual data centres using the equivalent of a small city's worth in electricity, and the demand for power from the tech sector predicted to increase fifteenfold in the next ten years. The computing hardware used is also extremely exploitative of natural resources, in particular water and rare earth metals, again magnified by the scale with which this hardware is being manufactured to meet demand.

Optical neural networks and neuromorphics aim to replicate some of the properties of traditional silicon-based ML using optical hardware, by encoding information in light and processing it through interactions with configurable optical elements. Examples include spatial light modulators (SLM) and random scattering media. Cameras can read out information from such a system, allowing for optical hardware and silicon computing to be paired in a system capable of solving general ML tasks.

Optical implementations of machine learning provide several advantages over silicon computing: Energy efficiency is improved by leveraging passive optical elements, and at the limit, single photons can be used as carriers of information. Calculations are performed through light-matter interaction as opposed to individual powered transistors in the case of silicon computing. These calculations are carried out at the speed of light in most cases, giving further advantage over silicon. The ability to easily scale optical hardware in three-dimensional space is also desirable, with silicon computing beginning to see scaling problems as Moore's Law starts to slow. The scope to introduce quantum effects is a clear advantage, giving options for solving classes of problem which may be intractable with classical computing.

This research aims to build optical neuromorphic systems to solve specific problems in imaging where optical setups are already in place, and systems which could provide advantage over silicon computing in established ML benchmarks. The primary challenge is developing efficient methods for training such systems in situ with minimal silicon computing overhead, where traditional optimisation techniques aren't feasible. This aims to mimic the natural learning processes which occur in biological systems such as the human brain.

Photonic neuromorphics sits at the junction between several highly active areas of research, including machine learning, quantum computing, imaging and even neuroscience. Bringing together developments in all of these areas will open new avenues of research and influence the future of machine learning computing hardware.