Heavy Ion Test Results for the MOOG Broad Reach BRE440 Processor

Schaefer, Justin J.; Troxel, Ian A.; Gruber, Matthew; Conger, Chris; Schaf, John; Narveson, Kent

doi:10.1109/redw.2019.8906285

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Long-duration crewed missions, space-based observatories, and solar system exploration will require highly reliable, fault-tolerant systems. Communication delays, the challenging orbital dynamics of Near-Earth Asteroids (NEAs), and extreme science missions require increased autonomy for on-board decision infrastructures [26]. Future robotic missions will involve greater complexity and reactivity, which will require increased reliance on autonomy (i.e., advanced onboard processing).…”

Section: Mission Scenarios and Applicationmentioning

confidence: 99%

Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space

Bruhn

Tsog

Kunkel³

et al. 2020

CEAS Space J

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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.

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Section: Mission Scenarios and Applicationmentioning

confidence: 99%