Fourier transforms are mathematical calculations that can be used to identify the frequencies that make up a signal and shed light on their individual contributions. These transforms have been used, for example, to unveil patterns in sounds and images, ranging from gradual changes in intensity to fine repeating motifs or details.

Researchers at Sandia National Laboratories, Infineon Technologies and Arizona State University recently demonstrated a new approach for efficiently performing crucial FFT calculations with an analog system made up of a large array of memory components.

Their proposed method, introduced in a paper published in Nature Electronics, entails dividing a large Fourier transform into smaller calculations and then leveraging the electrical properties of memory cells to perform key operations in parallel on an analog chip, while completing other steps digitally.

Whatever the specific architecture used here it is, it is worth taking a step back and reminding yourself that the Fourier Transform is not inherently Discrete and Bandwith Limited, rather it is just that in practice computers always end up doing Discrete Fourier Transforms.

What is going on here then is likely that the Fourier Transform is, through the sleight-of-hand magic trickery of computers being kept non-discrete (or briefly made non-discrete during some optimal step of the processing chain) longer into the process of the calculation than traditional computers and their hardware do (which is start from the beginning in discrete terms and never deviate from them).

*I mean discrete in the sense of whole numbers/measurements made in units vs. the analog realm which is composed of measurements along continuums to some degree of precision.