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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

SEE Rate Estimation: Model Complexity and Data Requirements

Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.

Ladbury, Ray↗
NASA NTRS2010

Considerations for a Proton Single Event Effects (SEE) Guideline

The intent of this document is to provide guidance on when and what type of SEE tests should be performed on a device under test (DUT) based on orbit, technology, existing data, and application. It is NOT intended to provide a detailed guideline for how to perform proton SEE radiation tests on electronics.

LaBel, Kenneth A.↗
NASA NTRS2010

Considerations for a Proton Single Event Effects (SEE) Guideline

The intent of this document is to provide guidance on when and what type of -SEE tests should be performed on a device under test (OUT) based on orbit, technology, existing data, and application. It is NOT intended to provide a detailed guideline for how to perform proton SEE radiation tests on electronics.

LaBel, Kenneth A.↗
NASA NTRS2018

What's It like to See Earth from Space? Viewing Your World with NASA's Worldview!

When you first see Earth from space, you'll realize it's largely covered in white - our world is quite cloudy! Look closer and you'll discern landmasses, oceans, and regions covered in snow. Look closer still and you'll notice that our world is in constant motion - storms brewing and tracing paths over the oceans, plumes of smoke from wildfires and plumes of ash from volcanic eruptions billowing with the wind, dust storms blowing across the deserts, phytoplankton swirling in the oceans, icebergs floating in the oceans, and you'll see the human footprint on the earth's surface: cities connected by roads and vast swaths of agriculture. NASA's Worldview (https://worldview.earthdata.nasa.gov) interactive web map application provides a platform to view the world as it has been every day for the past 18 years, using data from NASA's fleet of Earth Observing System (EOS) satellites.This presentation will cover the history and development of the Worldview web map application; the 700+ imagery layers that are provided by the Global Imagery Browse Services (GIBS) (https://earthdata.nasa.gov/gibs); current and new features that are in Worldview to constantly improve the user experience; the interdisciplinary nature of the app and how it helps a broad range of user communities discover and interact with NASA satellite imagery; and ongoing efforts to improve Worldview and serve user communities.

Earth Science↗
NASA NTRS2018

Guideline for Single-Event Effect (SEE) Testing of System on a Chip (SOC) Devices

The use of complex single and multicore processors with significant cache memory, on-chip peripherals, memory controllers, and high speed input/output (IO) that integrate many of the parts of a traditional computer system is becoming more common in space applications. Such devices are often referred to as system on a chip devices (SOCs), even though the term is used somewhat inaccurately due to the lack of analog and mixed signal subcircuits. These devices are complex combinations of single- or multi-core processors with memory controllers, high-speed input/output (IO), and other peripheral structures that formerly would have been handled by off-chip resources. In the past the processors were tested for single event effects (SEE) separately, and the peripherals were often put into custom application-specific integrated circuits (ASICs) along with other resources required by the user. Performance and cost pressures have pushed commercial devices to incorporate many of the functional blocks into a single chip, an SOC. The NASA Electronic Parts and Packaging Program (NEPP) has been examining ways to perform SEE radiation hardness assurance (RHA) testing of these processor-centric SOCs to achieve reasonable understanding of their performance in space missions.

Guertin, Steven M.↗
NASA NTRS2020

Threats to Resiliency of Redundant Systems Due to Destructive SEE

Destructive SEE pose serious challenges for the reliable use of COTS devices in space systems. We used system-level modeling to determine SEL rates that would likely compromise system reliability, resilience and capabilities. We then assembled a representative dataset of COTS CMOS parts and used nonparametric statistical techniques to assess the threat posed to redundant systems by destructive SEE.

single-event effects↗
NASA NTRS2017

SEE Test Results for the Snapdragon 820

SEE test results are presented for proton, neutron, and heavy ion testing of the Qualcomm Snapdragon 820 and its support DDR4 device (in this case the SK Hynix 24 Gb LP DDR4 device H9HKNNNDGUMUBR-NMH). Processor crashes and DDR4 stuck bits are the primary SEE types for protons and neutrons. Test preparation difficulties and software limitations caused test efforts to be limited to processor crashes, SEFIs and SBU, and Stuck Bits in the DDR4 device. Interpretation of results is complicated by mixing of errors between devices.

Cui, Matthew↗
NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗
NASA NTRS2011

Single-Event Effect (SEE) Survey of Advanced Reconfigurable Field Programmable Gate Arrays: NASA Electronic Parts and Packaging (NEPP) Program Office of Safety and Mission Assurance

The NEPP Reconfigurable Field-Programmable Gate Array (FPGA) task has been charged to evaluate reconfigurable FPGA technologies for use in space. Under this task, the Xilinx single-event-immune, reconfigurable FPGA (SIRF) XQR5VFX130 device was evaluated for SEE. Additionally, the Altera Stratix-IV and SiliconBlue iCE65 were screened for single-event latchup (SEL).

Xilinx Single-Event Effects (SEE) Test Consortium↗
NASA NTRS2012

Initial SEE Testing of Maestro

We have reported on initial SEE sensitivity of the full 49-core Maestro device. Supporting the low-level structures and qualitative system observation goals of phase 1 of testing. Observed sensitivities found to be consistent with Boeing predictions. Highlighted by the L1 data cache sensitivity which drives the rates on the current Maestro device. Presented details to the hardware and software setups that show where the limitations - highlighting future work. Key future work includes testing with memory and IO ports.

cache sensitivity↗
NASA NTRS2017

Considerations for GPU SEE Testing

This presentation will discuss the considerations an engineer should take to perform Single Event Effects (SEE) testing on GPU devices. Notable topics will include setup complexity, architecture insight which permits cross platform normalization, acquiring a reasonable detail of information from the test suite, and a few lessons learned from preliminary testing.

NASA Electronic Parts and Packaging (NEPP) Program↗