SPLEAT: SPiking Low-power Event-based ArchiTecture for in-orbit processing of satellite imagery

Nassim Abderrahmane, Benoît Miramond, Erwann Kervennic, Adrien Girard

Abstract: In this paper, we present SPLEAT, a SPiking Low-power Event-based ArchiTecture for the hardware deployment of Spiking Neural Networks (SNN). SPLEAT is designed as a configurable architecture that allows integrating several hardware modules representing different neural layers and, therefore, gives the ability to deploy a wide range of deep convolutional SNN topologies. Thanks to its event-based structure, SPLEAT takes advantage of the asynchronous spiking behavior to realize prediction efficiently. SPLEAT has been synthesized on a Cyclone V Field-Programmable Gate Array (FPGA) embedded on OPSSAT, a 3U nano-satellite launched on December 18, 2019 by the European Space Agency (ESA). SPLEAT has been used to perform in-orbit binary cloud classification on images provided by a 50 meters resolution sensor, which is still operational to this date. A second goal of the in-orbit experiment was to confront bio-inspired AI with classical deep learning. Consequently, in addition to SNNs executed using SPLEAT, standard Convolutional Neural Networks (CNNs) have been deployed on the same hardware target and evaluated on the same satellite images. The comparison between these neural network accelerators reveal a notable gain in terms of resources occupation for SPLEAT, reducing them by a factor of 6.62 for equivalent classification performances and an FPGA power consumption reduced by a factor of 5.91. To our best knowledge, this in-orbit experiment constitutes a world premiere in the fields of bio-inspired neural networks and aerospace.