Abstract
The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imaging. Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links. Many missions and applications can benefit significantly from onboard cloud computing similarly to Earth-based cloud services. Hence, enabling space systems to provide near real-time data and enable low latency distribution of critical and time sensitive information to users. In addition, the downlink capability can be more effectively utilized by applying more onboard processing to reduce the data and create high value information products. This paper discusses current implementations and roadmap for leveraging high performance computing tools and methods on small satellites with radiation tolerant hardware. This includes runtime analysis with benchmarks of convolutional neural networks and matrix multiplications using industry standard tools (e.g., TensorFlow and PlaidML). In addition, a ½ CubeSat volume unit (0.5U) (10 × 10 × 5 cm3) cloud computing solution, called SpaceCloud™ iX5100 based on AMD 28 nm APU technology is presented as an example of heterogeneous computer solution. An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.
Highlights
There are numerous studies and argumentation for increased onboard autonomy and data information processing to provide more efficient use of the relatively limited communication link bandwidth on small satellites [1,2,3]
For the purpose of demonstrating a real implementation of the architecture, a Qseven compute solution in a 1⁄2 CubeSat volume unit (0.5U) (10 × 10 × 5 cm3) called SpaceCloudTM iX5 is presented as an example of a heterogeneous computing solution suitable for spaceflight that provides advanced onboard processing for space systems
It is important to note that the AMD high-performance computing (HPC) software stack Radeon Open Compute (ROCm) is not possible to run on SOC/accelerated processing units (APUs) after version 1.7 without modification
Summary
There are numerous studies and argumentation for increased onboard autonomy and data information processing to provide more efficient use of the relatively limited communication link bandwidth on small satellites [1,2,3]. Projects in the mid-technology readiness level (TRL) range within the General Study Technology Programme (GSTP) Element 1 “Develop” AI 2019 compendium [5] These candidate projects are of strategic importance to current and future space systems and space exploration and cover both data exploitation and operations. The commonality with hardware and software development environments on ground is important to simplify deployment of AI in space systems It is important for cost and resource sharing reasons, where existing code from industry or consumer business can be reused and a wider access to talent is possible. The authors have explored edge computing and especially onboard AI data processing since 2013, leading up to a scalable radiation tolerant heterogeneous architecture first implemented using AMD 1st generation (28 nm) G-series System-on-Chip (SOC) paired with MicroSemi FPGA on an Input/output (IO) expanded industrial Qseven form factor board [6]. This paper expands on the previous work to include a full heterogeneous computer architecture for AMD 2nd generation (28 nm) G-series SOC, AMD R-series (28 nm) SOC, and the latest AMD V1000 Series (14 nm) SOC [7, 9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
