Conventional computing is rapidly approaching the physical limits of what can be achieved with silicon electronics. As the size of the transistors used to create complex logic-solving circuits shrinks ever further, gains in performance become outweighed by growing electrical power and thermal management requirements…
Our technology is designed to perform a mathematical function called a 2-dimensional Fourier transform at very high speed. In our system, this calculation is performed through a combination of optical interference and the properties of simple convex lenses…
Optalysys use silicon photonics to prepare a 2-dimensional field of optical information for nearly instantaneous parallel processing. Light must leave our silicon system so that it can perform a calculation through diffraction…
In this third article we’ll be talking about the last stage of the process, in which we extract the relevant information from the optical field after processing and convert it back into a form that can be understood and managed by a digital computer…
The optical method of computing the Fourier transform is extremely fast, but such systems will always have some physical limits to the number of data points they can work with at once. Light is a continuous medium but the individual components in our system that emit and detect light for processing are discrete and finite in number, so we’re faced with a very important question; can we perform a Fourier transform on a bigger array than the maximum resolution of a Fourier-optical computing device..?
What do post-quantum cryptography, convolutional neural networks, computational fluid dynamics, and signal analysis have in common? They all share an important property: each of them requires a lot of compute power to unleash their full potential and solve practical problems. But there is something else which unites them: they all can make use of the same mathematical operation to become more efficient. This operation is the Fourier transform (FT).
The size and complexity of AI models is vastly outpacing the rate at which baseline computing performance is growing. If we are to make use of mass-scale AI in the future, the solutions are stark: use exponentially more power on general-purpose hardware, or reach for more efficient and specialised hardware in the form of AI accelerators…
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