Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.

ABSTRACT: Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial se

SUBMITTER: Forno E 

PROVIDER: S-EPMC9811205 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature