AI/ML for Mission Processing Onboard Satellites
Mark J. Veyette, Kevin Aylor, Dan Stafford, M. Rios Herrera, Sajit Jumani, Cole Lineberry, Chris Macklen, E. Jane Maxwell, Randy Stiles, Matt Jenkins · AIAA SCITECH 2022 Forum · 2022
View Video Presentation: https://doi.org/10.2514/6.2022-1472.vid As new emerging threats require faster than human-in-the-loop response times, next generation defense systems are requiring more autonomy, data processing, and decision making at the edge. These new systems are looking to artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomous command and control. For space systems, onboard processing of advanced AI/ML algorithms, especially deep learning algorithms, requires a multiple magnitude increase in compute capability compared to what is available with legacy, radiation-tolerant, space-grade processors on current space vehicles. The next generation of space processors for AI/ML onboard will likely include a diverse landscape of heterogeneous systems including various combinations of CPUs, GPUs, FPGAs, and purpose-built ASICs. In this manuscript, we identify the driving requirements for AI/ML processing onboard; detail the similarities and differences between the ground, edge, and space environments for AI/ML; define a reference architecture and the services required to provide an end-to-end framework for developing and deploying AI/ML applications for space; and evaluate the hardware landscape for current and next-generation space AI processors.