Time-coded Spiking Fourier Transform in Neuromorphic Hardware
This work addresses the problem of improving signal processing efficiency for applications like automotive radar using neuromorphic computing, representing an incremental advancement in adapting algorithms to this emerging paradigm.
The authors tackled the need for efficient processing systems by proposing a time-based spiking neural network mathematically equivalent to the Fourier transform, implemented on the neuromorphic chip Loihi and validated in experiments with an automotive radar across five real scenarios.
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.