Energy efficiency analysis of Spiking Neural Networks for space applications

TL;DR

This paper evaluates the energy efficiency of Spiking Neural Networks (SNN) for space applications, demonstrating their potential for low-power onboard scene classification and developing a metric to predict energy consumption across hardware platforms.

Contribution

It introduces a numerical analysis of SNN techniques for space tasks, emphasizing temporal coding models and a new hardware-agnostic energy prediction metric.

Findings

SNNs show promising energy efficiency for space scene classification.

A novel metric effectively predicts energy consumption on neuromorphic hardware.

Temporal coding enhances sparsity and efficiency in SNN models.

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.