Time-coded Spiking Fourier Transform in Neuromorphic Hardware

TL;DR

This paper introduces a time-coded spiking neural network equivalent to the Fourier transform, implemented on neuromorphic hardware, demonstrating its effectiveness in processing radar signals efficiently.

Contribution

It presents a novel time-based spiking neural network for Fourier transform computation, implemented on neuromorphic hardware, enabling efficient signal processing.

Findings

Validated algorithm on automotive radar data

Demonstrated efficiency over traditional digital processors

Encouraged neuromorphic chip design for signal processing

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.