Linear transforms — like a Fourier transform — are a key math tool in engineering and science. A team from UCLA recently published a paper describing how they used deep learning techniques to design an all-optical solution for arbitrary linear transforms. The technique doesn’t use any conventional processing elements and, instead, relies on diffractive surfaces. They also describe a “data free” design approach that does not rely on deep learning.
There is obvious appeal to using light to compute transforms. The computation occurs at the speed of light and in a highly parallel fashion. The final system will have multiple diffractive surfaces to compute the final result.
The deep learning the paper’s authors refer to was all set up with TensorFlow using the Adam optimizer. It appears that the paper relies on simulations of the diffraction surfaces, not an actual implementation. We aren’t sure how hard it is to realize high-resolution diffraction surfaces with the very specific patterns called for by the designs.
If you are looking to get started with TensorFlow yourself, we’ve covered quite a few tutorials. On the other hand, we talk quite a bit about Fourier transforms, too.
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