This paper describes a reconfigurable architecture for an on-board processor to be used in space exploration critical systems. It relies on ARTICo3, a dynamically reconfigurable multi-accelerator hardware architecture that provides transparent reconfiguration and scalable performance, dependability, and power consumption, at run-time. The architecture is integrated under an RTEMS operating system, which manages reconfiguration and fault mitigation in a fully-compatible way with space requirements. In this work, the proposed processor is used to implement a vision-based navigation system, providing fully autonomous adaptation to different phases of a space mission timeline or events that the spacecraft may encounter during its lifespan. Results show that reconfigurability enables the practical usage of Commercial Off-The-Shelf (COTS) Multiprocessor Systems-on-Chips (MPSoCs) in real scenarios.

This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manages the losses the compressor may introduce to achieve higher compression ratios without compromising the recovered image quality. The algorithm has been implemented using High-Level Synthesis (HLS) techniques to increase design productivity by raising the abstraction level. The proposed lossy compression solution is deployed onto ARTICo³, a dynamically reconfigurable multi-accelerator architecture, obtaining a run-time adaptive solution that enables user-selectable performance (i.e., load more hardware accelerators to transparently increase throughput), power consumption, and fault tolerance (i.e., group hardware accelerators to transparently enable hardware redundancy). The whole compression solution is tested on a Xilinx Zynq UltraScale+ Field-Programmable Gate Array (FPGA)-based MPSoC using different input images, from multispectral to ultraspectral. For images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the proposed implementation renders an execution time of approximately 36 s when 8 accelerators are compressing concurrently at 100 MHz, which in turn uses around 20% of the LUTs and 17% of the dedicated memory blocks available in the target device. In this scenario, a speedup of 15.6× is obtained in comparison with a pure software version of the algorithm running in an ARM Cortex-A53 processor.