Institute for Communication Technologies and Embedded Systems

Spiking Neural Networks for Signal Classification with Digital and Analog Neuromorphic Systems: A Comparative Study

Happek, L. ,  Huang, J.-T. ,  Garcia Gener, A. ,  Leupers, R.Galicia, M.
Book Title:
The International Joint Conference on Neural Networks (IJCNN)
accepted for publication
Jul. 2024
Spiking Neural Networks (SNNs) have suitable properties
for realizing efficient processing at the edge, while having
several similarities to brain-inspired computing that can be
applied in neuromorphic computing systems. On the other hand,
signal processing for wireless communication is an essential application
that is critical at the edge. Signal modulation detection
is a task that requires reliable accuracy in noisy environments,
such as commercial and defense avionics applications. This
task can be performed in a neuromorphic system running an
SNN classification model using two different technologies: digital
or analog. We present a comparative study between a digital
manycore-based and an analog memristor-based neuromorphic
implementation. We use Intel’s LAVA and NeuroPack opensource
frameworks to implement them and compare them in
terms of accuracy. The results show that the digital technology
achieves relevant accuracies, while the analog technology requires
a more elaborate portability model to achieve comparable results.