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IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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Comparison of Multi-Phase Power Converters and Power Delivery Networks for Next-Generation Space Architectures

Comparison of Multi-Phase Power Converters and Power Deliver...

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IEEE Conference on Aerospace

作者： Nicholas Franconi Thomas Cook Christopher Wilson Alan D. George NASA Goddard Space Flight Center Science Data Processing Branch Code 587 Software Engineering Division Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh

Next-generation spacecraft developers are earnestly investigating the application of artificial intelligence (AI) algorithms onboard to enable new mission concepts for space exploration and science. However, the current generation of radiation-hardened processors are inferior compared to commercial-off-the-shelf alternatives in terms of the computational performance required by modern AI applications. To address this disparity, space-system designers have started employing novel radiation-tolerant architectures combining both commercial and radiation-hardened components to mitigate radiation effects at a system level. Unfortunately, developing single-board computers with radiation-tolerant, high-performance processors is challenging because designers must balance the sparce selection of radiation-hardened power converters and high-reliability decoupling capacitors with SmallSat/CubeSat area constraints and limited thermal conduction. Consequently, the next-generation of space processors, including the AMD-Xilinx Versal Adaptive Compute Acceleration Platform require demanding power solutions capable of supplying core rails with 0.8V ± 18mV and currents up to 150 A. In this paper, we present a multifaceted analysis of the power system and decoupling network for a future Versal-based design. We developed preliminary power estimates based on expected processor and FPGA resource utilization for common AI processing applications. These estimates drive the power system requirements and a comparative analysis of single-phase integrated converters and multi-phase discrete converters for high-current FPGA supplies. We develop a tradespace between the number of phases, input voltage, load current, switching frequency, power efficiency, and printed circuit board area. Finally, we design and simulate four power delivery networks based on commercial, high-reliability, and flight-qualified capacitors to compare the efficacy of 0201 decoupling capacitors in flight missions.

关键词： Rails Program processors Capacitors Voltage Power systems Resource management Artificial intelligence

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Evaluation of Xilinx Versal Architecture for Next-Gen Edge Computing in Space

Evaluation of Xilinx Versal Architecture for Next-Gen Edge C...

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IEEE Conference on Aerospace

作者： Noah Perryman Christopher Wilson Alan George NSF SHREC Center University of Pittsburgh Pittsburgh PA USA Science Data Processing Branch Code 587 NASA Goddard Space Flight Center Greenbelt MD United States NSF SHREC Center University of Pittsburgh Pittsburgh PA United States

Space edge computing has unique considerations (e.g., size, power, space radiation, etc.) that limit the performance capabilities of achievable onboard processing. Due to these restrictions, current state-of-the-art devices for space edge computing are unable to meet the resource and performance requirements for next-gen communication, navigation, and artificial intelligence (AI) applications planned for future science and defense missions. To address these issues, space designers are considering domain-specific architectures (DSAs) with specialized acceleration hardware, such as the Xilinx Versal Adaptive Compute Acceleration Platform (ACAP) architecture. This platform is heterogeneous and provides developers with scalar/vector processing units and programmable logic for mission-specific customization. In this paper, the performance and power-efficiency tradeoffs of different Versal ACAP processing subsystems (i.e., AI Engines, ARM Cortex-A72, ARM Cortex-R5F, and programmable logic) were evaluated by comparing execution of representative space applications. These experiments included three convolutional neural network (CNN)-based image-classification applications (i.e., MobileNetV1, ResNet-50, and GoogLeNet) for AI and a multitaper spectral estimation application for communication. Notably, the vector-based Versal AI Engines showed a promising performance improvement over the ARM Cortex-A72 and Cortex-R5F while usually being outperformed by the programmable logic. However, the AI Engines typically consumed significantly less power, with some AI engine applications being 1.48× more energy efficient compared to the programmable logic while not losing static power to excessive resource utilization. Overall, this evaluation demonstrates the versatility and heterogeneity of the Versal architecture and the tradeoffs between the on-chip subsystems. Therefore, the Versal will be considered for a future single-board computer to address challenges set by demanding next-gen s

关键词： Performance evaluation Program processors Space missions Estimation Computer architecture Energy efficiency Resource management

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Adaptively Lossy Image Compression for Onboard processing

Adaptively Lossy Image Compression for Onboard Processing

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IEEE Aerospace Conference

作者： Justin Goodwill David Wilson Sebastian Sabogal Alan D. George Christopher Wilson NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh 4420 Bayard St Suite 560 Pittsburgh PA Software Engineering Division NASA Goddard Space Flight Center Science Data Processing Branch Code 587 8800 Greenbelt Rd Greenbelt MD

ISBN: (数字)9781728127347

ISBN: (纸本)9781728127354

More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers. While these codecs reduce transmitted data volume, many are compute-intensive and require rapid processing to sustain sensor data rates. Emerging next-generation small satellite (SmallSat) computers provide compelling computational capability to enable more onboard processing and compression than previously considered. For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG);and (2) adaptive image compression through feature-point detection (FPD-JPEG). These algorithms rely on intelligent data-processing pipelines that adapt to sensor data to compress it more effectively, ensuring efficient use of limited downlink bandwidths. The first algorithm, CNN-JPEG, employs a hybrid approach adapted from literature combining convolutional neural networks (CNNs) and JPEG;however, we modify and tune the training scheme for satellite imagery to account for observed training instabilities. This hybrid CNN-JPEG approach shows 23.5% better average peak signal-to-noise ratio (PSNR) and 33.5% better average structural similarity index (SSIM) versus standard JPEG on a dataset collected on the Space Test Program - Houston 5 (STP-H5-CSP) mission onboard the International Space Station (ISS). For our second algorithm, we developed a novel adaptive image-compression pipeline based upon JPEG that leverages the Oriented FAST and Rotated BRIEF (ORB) feature-point detection algorithm to adaptively tune the compression ratio to allow for a tradeoff between PSNR/SSIM and combined file size over a batch of STP-H5-CSP images. We achieve a less than 1% drop in average PSNR and SSIM while reducing the combined file size by 29.6% compared to JPEG using a static quality factor (QF) of 90.

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ReCoN: A Reconfigurable CNN Acceleration Framework for Hybrid Semantic Segmentation on Hybrid SoCs for Space Applications

ReCoN: A Reconfigurable CNN Acceleration Framework for Hybri...

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IEEE Space Computing Conference (SCC)

作者： Sebastian Sabogal Alan George Gary Crum NSF SHREC Center University of Pittsburgh Pittsburgh PA USA Science Data Processing Branch (Code 587) NASA Goddard Space Flight Center Greenbelt MD USA

Recent advancements in deep learning present new opportunities for enhanced scientific methods, autonomous operations, and intelligent applications for space missions. Semantic segmentation is a powerful computer-vision process using convolutional neural networks (CNNs) to classify objects within an image. Semantic segmentation has numerous space-science and defense applications, from semantic labeling of Earth observations for insights about our changing planet, to monitoring natural disasters for damage control, to gathering intelligence for national defense and security. Despite these advantages, CNNs can be computationally expensive and prohibited on traditional radiation-hardened space processors, which are often generations behind their commercial-off-the-shelf counterparts in terms of performance and energy-efficiency. FPGA-based hybrid System-on-Chips (SoCs), which combine fixed-logic CPUs with reconfigurable-logic FPGAs, present numerous architectural advantages well-suited to address the computational capabilities required for high-performance, intelligent spacecraft. To enable semantic segmentation for on-board space processing, we propose a hybrid (hardware/software partitioned) approach using our reconfigurable CNN accelerator (ReCoN) for accelerating CNN inference on hybrid SoCs. When evaluated on the Xilinx Zynq SoC and Xilinx Zynq UltraScale+ MPSoC platforms, our hybrid approach demonstrates an improvement in performance and energy-efficiency up to two orders of magnitude compared to a software-only baseline on the hybrid SoC. Furthermore, fault injection and wide-spectrum neutron beam-testing was performed to characterize the ReCoN architectural response to injected errors and susceptibility to neutron irradiation.

关键词： Semantics Field programmable gate arrays Space missions Program processors Space vehicles Acceleration Image segmentation

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data exchange platform for digital economy applications 6

Data exchange platform for digital economy applications

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6th International Conference Information Technology and Nanotechnology. Session data science, ITNT-DS 2020

作者： Surnin, Oleg Ivaschenko, Anton Sitnikov, Pavel Stolbova, Anastasia Khorina, Anastasia Ilyasova, Nataly Ipsi Sec Open Code Data Analysis Department Samara Russia Samara State Technical University Computer Science Department Samara Russia Faculty of Infocommunication Itmo University Saint-Petersburg Russia Samara National Research University Information Systems and Technologies Department Samara Russia Faculty of Infocommunication Itmo University Saint-Petersburg Russia Branch of Fsrc Crystallography Image Processing Systems Institute of Ras and Photonics" RAS Samara Russia

This paper describes an experience of the data exchange software platform practical use supporting the modern trends of Digital Economy. The platform was initially designed for the suppliers and customers of data sources providing the up-to-date technologies of big data processing as an online service. The platform is also open for software developers to upload new algorithms and technologies in order to help them to find new areas of application. There is presented architecture and its software implementation for an intermediary online platform capable of collecting, processing and analysis of various datasets. Modern companies being the members of digital economy can use this platform to process their data and produce business analytics. They can become both suppliers and providers of data, as well as develop and upload new customized algorithms. First results were achieved in the area of retail and social media analysis. Copyright © 2020 for this paper by its authors.

关键词： Electronic data interchange

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Disks in Nearby Young Stellar Associations Found Via Virtual Reality

arXiv

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arXiv 2022年

作者： Higashio, Susan Kuchner, Marc J. Silverberg, Steven M. Brandt, Matthew A. Grubb, Thomas G. Gagné, Jonathan Debes, John H. Schlieder, Joshua Wisniewski, John P. Slocum, Stewart Bans, Alissa S. Bhattacharjee, Shambo Biggs, Joseph R. Bosch, Milton K.D. Cernohous, Tadeas Doll, Katharina Luca, Hugo A. Durantini Enachioaie, Alexandru Griffith, Phillip Hamilton, Joshua Holden, Jonathan Hyogo, Michiharu Jung, Dawoon Lau, Lily Piñeiro, Fernanda Piipuu, Art Stiller, Lisa Nasa Goddard Space Flight Center Exoplanets and Stellar Astrophysics Laboratory Code 667 GreenbeltMD20771 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Mit Kavli Institute United States Nasa Goddard Space Flight Center Science Data Processing Branch Code 587 GreenbeltMD20771 United States Planétarium Rio Tinto Alcan Espace Pour la Vie 4801 av. Pierre-de Coubertin MontrealQC Canada Institute for Research on Exoplanets Université de Montréal Département de Physique C.P. 6128 Succ. Centre-ville MontrealQCH3C 3J7 Canada Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Homer L. Dodge Department of Physics and Astronomy University of Oklahoma 440 W. Brooks Street NormanOK73019 United States Department of Computer Science Johns Hopkins University United States Department of Physics Emory University 201 Dowman Drive AtlantaGA30322 United States French National Centre for Scientific Research France Disk Detective IATE-OAC Universidad Nacional de Córdoba-CONICET Laprida 854 CórdobaX5000 BGR Argentina Meisei University 2-1-1 Hodokubo Tokyo Hino191-0042 Japan

The Disk Detective citizen science project recently released a new catalog of disk candidates found by visual inspection of images from NASA's Wide-Field Infrared Survey Explorer (WISE) mission and other surveys. We applied this new catalog of well-vetted disk candidates to search for new members of nearby young stellar associations (YSAs) using a novel technique based on Gaia data and virtual reality (VR). We examined AB Doradus, Argus, β Pictoris, Carina, Columba, Octans-Near, TucanaHorologium, and TW Hya by displaying them in VR together with other nearby stars, color-coded to show infrared excesses found via Disk Detective. Using this method allows us to find new association members in mass regimes where isochrones are degenerate. We propose ten new YSA members with infrared excesses: three of AB Doradus (HD 44775, HD 40540 and HD 44510), one of β Pictoris (HD 198472), two of Octans-Near (HD 157165 and BD+35 2953), and four disk-hosting members of a combined population of Carina, Columba and Tucana-Horologium: CPD-57 937, HD 274311, HD 41992, and WISEA J092521.90-673224.8. This last object (J0925) appears to be an extreme debris disk with a fractional infrared luminosity of 3.7 × 10-2. We also propose two new members of AB Doradus that do not show infrared excesses: TYC 6518-1857-1 and CPD-25 1292. We find HD 15115 appears to be a member of Tucana-Horologium rather than β Pictoris. We advocate for membership in Columba-Carina of HD 30447, CPD-35 525, and HD 35841. Finally, we propose that three M dwarfs, previously considered members of Tuc-Hor are better considered a separate association, tentatively called "Smethells 165". © 2022, CC BY.

关键词： Stars

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Scientific visualization to study Flux Transfer Events at the Community Coordinated Modeling Center

Scientific visualization to study Flux Transfer Events at th...

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作者： Rastätter, Lutz Kuznetsova, Maria M. Sibeck, David G. Berrios, David H. Space Weather Laboratory Mail Code 674 NASA GSFC Greenbelt MD 20771 United States Science Data Processing Branch Mail Code 587 NASA GSFC Greenbelt MD 20771 United States

In this paper we present recent additions to the visualization toolset offered by the Community Coordinated Modeling Center (CCMC). Two suites of visualization tools are available that can address different needs during the analysis of model simulations of the magnetosphere that are provided by the CCMC. The online, server-side visualization allows the user to quickly browse through simulation runs and now can create maps of magnetic field line topology in the magnetosphere. The second tool, SWX, can be used on the client computer after data have been downloaded. With this second tool the user can interact directly with the three-dimensional objects that are being rendered. We present results from a simulation of a Flux Transfer Event that was performed at the CCMC using a magnetohydrodynamic model of the Earth's magnetosphere with a high resolution grid focused on the dayside magnetosheath and dayside magnetopause. The simulation shows that the FTE that results from localized magnetic reconnection is a complicated three-dimensional structure that requires modern visualization techniques. Visualization techniques that are presented here allow the researcher to fully appreciate the complexity contained in magnetospheric simulation results. © 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： data visualization

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