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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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Magnetoresistive Random Access Memory (MRAM) is much different from conventional types of memory like SRAM, DRAM, and Flash, where electric charge is used to store information. Instead of exploiting the charge of an electron, MRAM uses its spin to store data. This new type of electronics is known as "spintronics." The primary focus of this report is the current generation of MRAM technology, and its reliability, vendors, and space-readiness.
This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.
Magnetoresistive Random Access Memory (MRAM) is much different from conventional types of memory like SRAM, DRAM, and Flash, where electric charge is used to store information. Instead of exploiting the charge of an electron, MRAM uses its spin to store data. This new type of electronics is known as “spintronics.” The primary focus of this report is the current generation of MRAM technology, and its reliability, vendors, and space-readiness.
MRAM and DRAM memory technologies identified for possible radiation hardening were investigated for memory-array- and controller-level radiation sensitivity. TID and SEE performance of memory arrays and associated cells is good, but with some questions about viability of hardening of problematic control circuitry. Control-circuitry-related SEE problems are highlighted. TID data are focused on memory arrays only.
The Dragonfly Mass Spectrometer (DraMS) being developed at NASA’s Goddard Space Flight Center will use a solid-state 266-nm pulsed Nd:YAG laser perform compositional analysis on the surface of Titan. Due to the high fluence of the focused pulse energy on the laser’s beam steering unit (BSU) and the mass spectrometer window, the damage threshold of these optics in a Titan atmosphere needed to be characterized. This paper details the test setup and the successful demonstration of testing the highest fluence optics for the expected mission duration of 2 million laser pulses in a Titan-relevant atmosphere.
The Dragonfly Mass Spectrometer (DraMS) being developed at NASA’s Goddard Space Flight Center will use a solid-state 266-nm pulsed Nd:YAG laser to perform compositional analysis on the surface of Titan. Due to the high fluence of the focused pulse energy on the laser’s beam steering unit (BSU) and the mass spectrometer window, the damage threshold of these optics in a Titan atmosphere needed to be characterized. This paper details the test setup and the successful demonstration of testing the highest fluence optics for the expected mission duration of 2 million laser pulses in a Titan-relevant atmosphere.
This paper shows mathematically and experimentally that inline deduplication is not suitable for file systems on ultra-low latency Intel Optane DC PM devices in terms of performance, and proposes DeNova, an offline deduplication specially designed for log-structured NVM file systems such as NOVA. DeNova offers high-performance and low-latency I/O processing and executes deduplication in the background without interfering with foreground I/Os. DeNova employs DRAM-free persistent deduplication metadata, favoring CPU cache line, and ensures failure consistency on any system failure. We implement DeNova in the NOVA file system. Evaluation with DeNova confirms a negligible performance drop of baseline NOVA of less than 1%, while gaining high storage space savings. Extensive experiments show DeNova is failure consistent in all failure scenario cases.
Memory subsystems contribute significantly to the performance and energy efficiency of high-performance computing (HPC) applications. Traditional memory technologies with conventional organization (e.g., DRAM) are struggling to keep up with the increasing memory requirements of modern applications. Techniques such as multilayer cache hierarchy and out-of-order execution are still falling short of mitigating the penalty incurred by memory accesses. Processing-in-memory (PIM), which involves moving memory-intensive kernels to memory for execution instead of bringing the data to the processing unit, is emerging as a promising technique. PIM has recently received traction among computer architecture researchers, and the increasing research activity surrounding this technique indicates its potential to alleviate main memory performance bottlenecks. In this paper, we characterize and identify memory-intensive HPC kernels, perform a first-order evaluation of the PIM technique for selected HPC kernels, quantify performance deviation, and analyze the key factors that affect PIM efficiency.
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth required to process these NNs as fast as the data is being produced (e.g., every 25 ns). As such, these extreme latency and bandwidth requirements have architectural implications for the hardware intended to run these NNs: 1) all NN parameters must fit on-chip, and 2) codesigning custom/reconfigurable logic is often required to meet these latency and bandwidth constraints. In our work, we show that many scientific NN applications must run fully on chip, in the extreme case requiring a custom chip to meet such stringent constraints.
Breakdown of gate oxides from heavy ions is investigated.
Single event functionality interrupt (SEFI) results are presented for Hynix SDRAMS. The SEFI response threshold is below LET 9.9 Mev-cm(exp 2)/mg and the saturated cross section is 6 x 10(exp -5)cm(exp 2). Dynamic SEFI identification was made, and in-situ recovery restored functionality. Verification results of the identification algorithm are presented. An observed high current radiation response is also presented.