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arXiv · 2202.12650

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

Javier López-Randulfe·Nico Reeb·Negin Karimi·Chen Liu·Hector A. Gonzalez·Robin Dietrich·Bernhard Vogginger·Christian Mayr·Alois Knoll
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Abstract

After several decades of continuously optimizing computing systems, the Moore's law is reaching itsend. However, there is an increasing demand for fast and efficient processing systems that can handlelarge streams of data while decreasing system footprints. Neuromorphic computing answers thisneed by creating decentralized architectures that communicate with binary events over time. Despiteits rapid growth in the last few years, novel algorithms are needed that can leverage the potential ofthis emerging computing paradigm and can stimulate the design of advanced neuromorphic chips.In this work, we propose a time-based spiking neural network that is mathematically equivalent tothe Fourier transform. We implemented the network in the neuromorphic chip Loihi and conductedexperiments on five different real scenarios with an automotive frequency modulated continuouswave radar. Experimental results validate the algorithm, and we hope they prompt the design of adhoc neuromorphic chips that can improve the efficiency of state-of-the-art digital signal processorsand encourage research on neuromorphic computing for signal processing.

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Javier López-Randulfe, Nico Reeb, Negin Karimi, Chen Liu, Hector A. Gonzalez, Robin Dietrich, Bernhard Vogginger, Christian Mayr, Alois Knoll. 2022-03-31. Time-coded Spiking Fourier Transform in Neuromorphic Hardware. https://doi.org/10.1109/tc.2022.3162708

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