Investigating Spiking Neural Networks for Energy-Efficient On-Board AI Applications. A Case Study in Land Cover and Land Use Classification
Spiking neural networks have been attracting the interest of researchers due to their potential energy efficiency. This feature makes them appealing for applications on board CubeSats or small Earth observation satellites, given their strict energy consumption requirements. However, the performance of spiking neural networks in terms of the accuracy on the space-scene classification datasets, and their benefits with respect to the energy efficiency still remain to be demonstrated. This work is a preliminary investigation on deploying spiking neural networks to land cover and land use classification problems. To train a spiking model, a VGG-16-based artificial neural network has been trained on EuroSAT RGB benchmark dataset. The parameters of this network were then used to initialise a spiking model, which was optimised by finetuning the connection weights and the synaptic filters parameters. By using the mean neuron activations, and the number of time-steps and their width as proxies for the energy consumption of the models, this study shows different tradeoffs between accuracy, latency, and energy efficiency, when comparing a spiking model to the deep learning approach. Moreover, some additional input data preprocessing strategies are investigated as a method of a further enhancement of the energy benefits of the spiking models.