Neuromorphic-Compatible Spiking Neural Networks for Energy-Efficient Music Analysis

Amidst the escalating computational demands and associated energy concerns, this study investigates the potential of Spiking Neural Networks (SNNs) combined with neuromorphic devices as an energy-efficient alternative to traditional machine learning algorithms, with a focus on temporal data tasks. The energy inefficiencies of traditional machine learning algorithms are becoming increasingly evident. The synergy of SNNs and neuromorphic devices offers a promising avenue to address this challenge, specifically the optimization of temporal data analysis. We explored various neuromorphic-compatible SNN architectures and their associated training methodologies. Emphasis was placed on training the SNNs using backpropagation and evolutionary genetic algorithms, with music analysis serving as our chosen temporal medium for evaluation. Preliminary findings suggest that SNNs have the potential to achieve performance levels comparable to conventional neural networks in categorical tasks such as music genre recognition. Furthermore, the integration of these networks with neuromorphic devices indicates promising avenues for significant energy savings. While this research is currently a proof of concept, it underscores the potential efficacy of SNNs in temporal data analysis tasks. More broadly, it highlights the transformative possibilities of integrating SNNs with neuromorphic hardware, pointing towards a future with more sustainable and efficient computational solutions.