Entrenamiento de IA con Fibra Óptica: Un Salto Basado en Luz

Por qué la Fibra Óptica Podría Reemplazar la Electricidad en la Computación de IA

Since the early days of computación, almost all computers have been based on calculations using electricity in one way or another, from antique vacuum tubes to modern nanometer-scale silicon chips.

As silicon chips get smaller and smaller, researchers have been looking at new ways to build computers that could push our capacity further than silicon chips, a topic we explored in “Top 10 Non-Silicon Computing Companies”.

These methods include using different materials, like carbon carbide, vanadium dioxide, organic materials, or graphene, for example. Another way is to change how computing is done, moving away from the binary programming of electricity-based computing, which includes quantum computing and photonics.

Photonics uses light instead of electricity to encode and transfer information. However, until now, it has still been ultimately converted into a binary signal, failing to form a pure light-based form of computation.

This has changed with the work of researchers at the Tampere University (Finland) and Université Marie et Louis Pasteur (Besançon, France). They used optical fiber for ultrafast calculations and published their findings in the scientific journal Optics Letters¹, under the title “Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine”.

Limitaciones del Entrenamiento Tradicional de IA con Sistemas Electrónicos

AI training and data processing are reaching limits in terms of efficiency, with AI computation increasingly constrained by energy consumption and the speed of data processing.

In contrast, light-based calculations have the potential to be thousands of times quicker and can encode data into tiny differences of energy, making it more efficient. The issue is that so far, no direct calculation using light has been performed.

The researchers’ work used a particular class of computing architecture known as an Extreme Learning Machine, (ELM) an approach inspired by neural networks.

Among some of their advantages, ELMs can learn from the training data in one step and are a relatively simple algorithm.

As a rule, ELM is unlikely to be useful for very complex tasks requiring multiple layers of AI training, but can perform very well and more efficiently for specific tasks, like visual recognition for example.

Cómo los Investigadores Codificaron Imágenes Usando Fibra Óptica

The researchers used femtosecond laser pulses (a billion times shorter than a camera flash) and an optical fiber confining light in an area smaller than a fraction of human hair to build an optical ELM system.

The laser pulses are short enough to contain a large number of different wavelengths or colors, creating a rich dataset.

They then sent these data into the fiber with a relative delay encoded according to an image.

Fuente: Tampere University

El Papel de la Óptica No Lineal en el Procesamiento de IA

This form of data encoding was transformed by the nonlinear interaction of light and glass.

Linear optics is the regular optics taught in school, where the light directly interacts with a prism, for example.

In non-linear optics, the reaction of the medium in which the light passes depends on the light’s wavelength, intensity, direction, and polarization.

Nonlinear optical components can cause photons of different frequencies to combine and create new photons at new frequencies.

“En lugar de usar electrónica y algoritmos convencionales, la computación se logra aprovechando la interacción no lineal entre pulsos de luz intensos y el vidrio.”

Mathilde Hary and Andrei Ermolaev – Post-Doctoral Researchers

La interacción no lineal y el algoritmo Extreme Learning Machine (ELM) pudieron entrenar una IA para clasificar dígitos manuscritos (como los utilizados en el popular benchmark de IA MNIST).

Fuente: Optics Letters

Los mejores sistemas alcanzaron una precisión de más del 91 %, cercana a los métodos digitales de última generación.

Lo que hace que el resultado sea excepcional es que se logró en menos de un picosegundo, o una billonésima de segundo (0.000000000001 segundos).

Optimización Ideal

The best results did not occur at the maximum level of nonlinear interaction or complexity.

Instead, they required a delicate balance between fiber length, dispersion (the propagation speed difference between different wavelengths), and power levels.

“El rendimiento no es simplemente una cuestión de empujar más potencia a través de la fibra. Depende de cuán precisamente se estructure inicialmente la luz, es decir, cómo se codifica la información y cómo interactúa con las propiedades de la fibra.”

Mathilde Hary – Post-Doctoral Researcher

¿Son las Computadoras de Fibra Óptica el Futuro de la IA?

Training AIs with only light is a radical departure from all the methods used until now. This is likely not going to be a method possible to use for every type of data, but for the ones where it can be applied, this could bring results that are 1,000x more efficient in terms of energy, and as much as a million times quicker.

“Our models show how dispersion, nonlinearity and even quantum noise influence performance, providing critical knowledge for designing the next generation of hybrid optical-electronic AI systems.”

Andrei Ermolaev – Post-Doctoral Researcher

Most likely, such an approach would mean that some AI calculation would be delegated to a non-linear optical fiber hardware custom-built for the task. So repetitive tasks, like visual identification, would be the best candidates more than processing new data.

“This work demonstrates how fundamental research in nonlinear fiber optics can drive new approaches to computation. By merging physics and machine learning, we are opening new paths toward ultrafast and energy-efficient AI hardware.

Andrei Ermolaev – Post-Doctoral Researcher

Potential applications range from real-time signal processing to environmental monitoring and high-speed AI inference.

Such work is, however, still at the demonstration of basic principles of the technique stage, and far from a commercialization step.

It nevertheless demonstrates that photonics is likely going to be an increasingly important part of the computing industry moving forward, as light can be superior to electricity for some computing applications due to fundamental physics reasons.

Principales Empresas Cotizadas de Láser y Fotónica

Coherent (II-VI Marlow): Un Líder en Innovación Láser

Coherent is a large industrial conglomerate with 26,000+ employees and a leader in laser technology. It resulted from the merger of advanced material II-VI (COHR ) Marlow with laser maker Coherent.

The company is an expert in advanced materials used in lasers, optics, and photonics, such as indium phosphide, epitaxial wafers, and gallium arsenide.

It grew largely thanks to multiple acquisitions over the last decade, from $600M in revenues in 2013 to $4.7B in 2024.

The company derives 29% of its revenues from lasers directly, with the rest linked to associated equipment like optical fiber, and electronics. The instrumentation category mostly includes life

Fuente: Coherent

The presence of the company in advanced materials like thermophotovoltaics (which discutimos en un artículo anterior), silicon carbide, lasers, and electronics helps it benefit from structural trends like the growth of precision manufacturing, additive manufacturing (3D printing), electrification, and renewable energies.

The company has separado recientemente su negocio de carburo de silicio en una nueva entidad, propiedad en un 75 % de Coherent, with the rest owned equally by its partners Mitsubishi Electric (bringing silicon carbide power IP) and Denso (bringing its activity as an automotive supplier on electrification and power semiconductors).

This is because silicon carbide is increasingly its own technology, mostly used in high-power applications like EVs, batteries, and renewable energy.

Coherent is a leader en LIDAR y detección digital 3D, incluidas aplicaciones de conducción autónoma, biotech celdas de flujo de secuenciación de próxima generación (NGS), and láseres para la fabricación de semiconductores. It expects its main markets to grow at 8-20%.

Fuente: Coherent

The other potential new applications of lasers, like direct energy weapons, photonic computing, nuclear fusion, and spacetech, could all equally help sustain the long-term growth of the company.

Overall, Coherent is as close as it can get to a “pure play” publicly traded laser company for investors interested in the sector, with strong vertical integration and 3,100+ patents protecting its innovations.

As photonics progresses, it will progressively increase the demand for ultra-fast, ultra-precise laser systems, as well as lasers used in optical telecommunications.

Últimas Noticias y Desarrollos de la Acción Coherent (COHR)

Estudio Referenciado

^{1. Andrei V. Ermolaev, Mathilde Hary, et al. Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine. Optics Letters. Vol. 50, Número 13, pp. 4166-4169 (2025) https&#58//doi.org/10.1364/OL.562186}