Energy efficiency analysis of Spiking Neural Networks for space applications

• URL: http://arxiv.org/abs/2505.11418v1
• Date: Fri, 16 May 2025 16:29:50 GMT
• Title: Energy efficiency analysis of Spiking Neural Networks for space applications
• Authors: Paolo Lunghi, Stefano Silvestrini, Dominik Dold, Gabriele Meoni, Alexander Hadjiivanov, Dario Izzo,
• Abstract summary: Spiking Neural Networks (SNN) are highly attractive due to their theoretically superior energy efficiency.<n>This work presents a numerical analysis and comparison of different SNN techniques applied to scene classification for the EuroSAT dataset.
• Score: 43.91307921405309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the exponential growth of the space sector and new operative concepts ask for higher spacecraft autonomy, the development of AI-assisted space systems was so far hindered by the low availability of power and energy typical of space applications. In this context, Spiking Neural Networks (SNN) are highly attractive due to their theoretically superior energy efficiency due to their inherently sparse activity induced by neurons communicating by means of binary spikes. Nevertheless, the ability of SNN to reach such efficiency on real world tasks is still to be demonstrated in practice. To evaluate the feasibility of utilizing SNN onboard spacecraft, this work presents a numerical analysis and comparison of different SNN techniques applied to scene classification for the EuroSAT dataset. Such tasks are of primary importance for space applications and constitute a valuable test case given the abundance of competitive methods available to establish a benchmark. Particular emphasis is placed on models based on temporal coding, where crucial information is encoded in the timing of neuron spikes. These models promise even greater efficiency of resulting networks, as they maximize the sparsity properties inherent in SNN. A reliable metric capable of comparing different architectures in a hardware-agnostic way is developed to establish a clear theoretical dependence between architecture parameters and the energy consumption that can be expected onboard the spacecraft. The potential of this novel method and his flexibility to describe specific hardware platforms is demonstrated by its application to predicting the energy consumption of a BrainChip Akida AKD1000 neuromorphic processor.

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