  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.
