Estimating the Downlink Data-Rate of a CCSDS File Delivery Protocol IP Core

Meoni, Gabriele; Valverde, Alberto; Magistrati, Giorgio; Fanucci, Luca

doi:10.1007/978-3-030-37277-4_60

Cited by 4 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deployment of DNNs on board spacecraft might also mitigate the problem of the increasing number of sensor data that must be downloaded to ground [1,5,11]. Indeed, less usable data, like cloud-covered images, can be identified, tagged, pre-filtered, discarded, or selectively compressed [2,3].…”

Section: Introductionmentioning

confidence: 99%

An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study

et al. 2021

Self Cite

View full text Add to dashboard Cite

In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on board Earth Observation (EO) satellites might lead to numerous advantages in terms of mitigation of downlink bandwidth constraints, costs, and increment of the satellite autonomy. In this framework, the CloudScout project, funded by the European Space Agency (ESA), represents the first time in-orbit demonstration of a Convolutional Neural Network (CNN) applied to hyperspectral images for cloud detection. The first instance of this use case has been done with an INTEL Myriad 2 VPU on board a CubeSat optimized for low cost, size, and power efficiency. Nevertheless, this solution introduces multiple drawbacks due to its design not specifically being for the space environment, thus limiting its applicability to short-lifetime Low Earth Orbit (LEO) applications. The current work provides a benchmark between the Myriad 2 and our custom hardware accelerator designed for Field Programmable Gate Arrays (FPGAs). The metrics used for comparison include inference time, power consumption, space qualification, and components. The obtained results show that the FPGA-based solution is characterized by a reduced inference time, and a higher possibility of customization, but at the cost of greater power consumption and a longer Time to Market. As a conclusion, the proposed approach might extend the potential market of DL-based solutions to long-term LEO or interplanetary exploration missions through deployment on space-qualified FPGAs, with a limited cost in energy efficiency.

show abstract