Optical neural networks have the potential to handle complex computing tasks with high speed and large capacity. However, fully utilizing this potential will require further advancements, such as addressing the challenge of reconfigurability. A recent breakthrough by a research team has introduced a new approach to reconfigurable neuromorphic building blocks by incorporating sound waves into photonic machine learning. This involves using light to create temporary acoustic waves in an optical fiber. These sound waves could, for example, enable the implementation of recurrent functionality in a telecom optical fiber.
Understanding contextual information, such as language, is crucial. Optical neural networks have the potential to offer a fast and effective solution for complex computing tasks. However, unlocking their full capabilities will require further advancements. One of the challenges is the ability to reconfigure optical neural networks. A team of researchers at the Stiller Research Group at the Max Planck Institute for the Science of Light, working with the Englund Research Group at the Massachusetts Institute of Technology, has made progress in establishing a foundation for new reconfigurable neuromorphic building.
to these AI models is that they are heavily reliant on large data centers and consume significant amounts of energy. This is where photonic machine learning comes in. It offers an alternative by using light to process information more efficiently. The addition of sound waves to this process further enhances its capabilities and opens up new possibilities for applications in telecommunications and language interpretation. This innovative approach could potentially revolutionize the way we use and benefit from artificial intelligence.There is a huge demand for energy as these smart devices develop, which means that new solutions are needed to increase signal processing speed and decrease energy usage.
Neural networks have the potential to be the foundation of artificial intelligence. Creating them as optical neural networks, using light instead of electric signals, offers the ability to process large amounts of data at high speeds and with great energy efficiency. However, most experimental approaches to implementing optical neural networks have so far relied on fixed components and constant devices. Now, an international research team led by Bi has been working on a solution.At the Max-Planck Institute for the Science of Light, rgit Stiller and Dirk Englund from Massachusetts Institute of Technology have developed a method for creating reconfigurable building blocks for photonic machine learning using sound waves. They conducted their experiment using thin optical fibers, commonly used for high-speed internet connections worldwide.
The breakthrough lies in the generation of traveling sound waves using light, which then control the computational processes of an optical neural network. This allows optical information to be processed and linked to acoustic waves. These sound waves have a longer wavelength than the light waves, making them ideal for manipulating the network.The optical information stream travels at a faster transmission time than the sound waves, allowing it to remain in the optical fiber for a longer period. This enables it to be connected to each subsequent processing step. The unique aspect of this process is that it is entirely controlled by light and does not require complex structures or transducers.
“I am thrilled that we are delving into this new area of research, leading the way in using sound waves to control optical neural networks. Our research has the potential to inspire the development of innovative building blocks for new photonic computation architectures,” says Dr. Birgit Stiller, head of the Quantum Op.
The Acoustics Research Group has successfully demonstrated a crucial component known as a recurrent operator, a technology commonly utilized in recurrent neural networks. This operator enables the connection of multiple computational steps, giving each individual calculation step a context.
In simpler terms, the arrangement of words in a sentence can significantly impact its meaning. For instance, the sentences “She decided to research the challenge.” and “She decided to challenge the research.” use the same words but convey different meanings due to the distinct contexts created.
Implemented by the orders of the words, a typical fully-connected neural network on a computer encounters issues in capturing context as it needs access to memory. To address this challenge, neural networks have been enhanced with recurrent operations that allow for internal memory and the capturing of contextual information.
While these recurrent neural networks are easy to implement digitally, their implementation in optics is challenging and has previously relied on artificial cavities for memory provision.
The researchers have now utilized sound waves to implement.The development of a recurring operator has led to the creation of the Optoacoustic REcurrent Operator (OREO), which utilizes the natural properties of an optical waveguide, eliminating the need for artificial reservoirs or new structures. OREO allows for complete optical control, enabling the optoacoustic computer to be programmed on a pulse-by-pulse basis. For example, researchers have successfully implemented a recurring dropout optically for the first time, a regulation technique previously only used to enhance the performance of digital recurring neural networks. OREO has successfully distinguished up to 27 different patterns, showcasing its capabilities.The OREO’s all-optical control is a highly effective feature, particularly its ability to program the system on a pulse-by-pulse basis, which adds several new possibilities. Steven Becker, a doctoral student in the Stiller Lab, is enthusiastic about the disruptive potential of using sound waves for photonic machine learning and is eager to see how the field will advance in the future. This technology could lead to a new type of optical neuromorphic computing that can be reconfigured spontaneously, allowing for large-scale in-memory computing.The use of photonic machine learning has the potential to greatly improve the processing of information and make operations more energy-efficient, especially when combined with acoustic waves. Dr. Birgit Stiller believes that this approach could be an all-optically-controlled and easy-to-operate tool-kit for parallel processing of information. This method can also benefit on-chip implementations of optical neural networks in telecommunication networks. The implementation is possible in photonic waveguides without the need for additional electronic controls. The journal reference for this information is Steven Becker, Dirk Englund, Birgit Stiller’s work on “An optoacoustic field-programmable.”Perceptron for recurrent neural networks. The study was published in Nature Communications in 2024, volume 15, issue 1. The DOI for the article is 10.1038/s41467-024-47053-6.