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Results for “memory mapping”

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 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Parallel simulated annealing algorithms for cell placement on hypercube multiprocessors

Two parallel algorithms for standard cell placement using simulated annealing are developed to run on distributed-memory message-passing hypercube multiprocessors. The cells can be mapped in a two-dimensional area of a chip onto processors in an n-dimensional hypercube in two ways, such that both small and large cell exchange and displacement moves can be applied. The computation of the cost function in parallel among all the processors in the hypercube is described, along with a distributed data structure that needs to be stored in the hypercube to support the parallel cost evaluation. A novel tree broadcasting strategy is used extensively for updating cell locations in the parallel environment. A dynamic parallel annealing schedule estimates the errors due to interacting parallel moves and adapts the rate of synchronization automatically. Two novel approaches in controlling error in parallel algorithms are described: heuristic cell coloring and adaptive sequence control.

Banerjee, Prithviraj↗

DOE OSTI2023

A reference-area-free strain mapping method using precession electron diffraction data

Here, in this work, we developed a method using precession electron diffraction data to map the residual elastic strain at the nano-scale. The diffraction pattern of each pixel was first collected and denoised. Template matching was then applied using the center spot as the mask to identify the positions of the diffraction disks. Statistics of distances between the selected diffracted disks enable the user to make an informed decision on the reference and to generate strain maps. Strain mapping on an unstrained single crystal sapphire shows the standard deviation of strain measurement is 0.5%. With this method, we were able to successfully measure and map the residual elastic strain in VO 2 on sapphire and martensite in a Ni 50.3 Ti 29.7 Hf 20 shape memory alloy. This approach does not require the user to select a “strain-free area” as a reference and can work on datasets even with the crystals oriented away from zone axes. This method is expected to provide a robust and more accessible alternative means of studying the residual strain of various material systems that complements the existing algorithms for strain mapping.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1992

Structure and strategy in encoding simplified graphs

Tversky and Schiano (1989) found a systematic bias toward the 45-deg line in memory for the slopes of identical lines when embedded in graphs, but not in maps, suggesting the use of a cognitive reference frame specifically for encoding meaningful graphs. The present experiments explore this issue further using the linear configurations alone as stimuli. Experiments 1 and 2 demonstrate that perception and immediate memory for the slope of a test line within orthogonal 'axes' are predictable from purely structural considerations. In Experiments 3 and 4, subjects were instructed to use a diagonal-reference strategy in viewing the stimuli, which were described as 'graphs' only in Experiment 3. Results for both studies showed the diagonal bias previously found only for graphs. This pattern provides converging evidence for the diagonal as a cognitive reference frame in encoding linear graphs, and demonstrates that even in highly simplified displays, strategic factors can produce encoding biases not predictable solely from stimulus structure alone.

Schiano, Diane J.↗

DOE OSTI2020

Combinatorial Exploration and Mapping of Phase Transformation in a Ni–Ti–Co Thin Film Library

Combinatorial synthesis and high-throughput characterization of a Ni–Ti–Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (R(T)) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Finally, composition–structure–property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni–Ti–Co thin film library.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm’s parameters considering these trade-offs. We provide a sequential and a parallel implementation of the proposed algorithm. We perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68× memory savings. On massive datasets we demonstrate that GPU-accelerated Picasso can process inputs with 49.5× more Pauli strings (vertex set in our graph) and 2,478× more edges than state-of-the-art parallel approaches.

artificial intelligence, quantum computing↗

DOE OSTI2025

CryoTEN: efficiently enhancing cryo-EM density maps using transformers

Abstract Motivation Cryogenic electron microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map-sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. Results In this study, we introduce CryoTEN—a 3D UNETR++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, automatic de novo protein structure modeling shows that protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running >10 times faster and requiring much less GPU memory than them. Availability and implementation The source code and data are freely available at https://github.com/jianlin-cheng/cryoten.

Biochemistry & Molecular Biology↗

NASA NTRS1989

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Gomez, Fernando↗

NASA NTRS2004

Accessing FMS Functionality: The Impact of Design on Learning

In modern commercial and military aircraft, the Flight Management System (FMS) lies at the heart of the functionality of the airplane. The nature of the FMS has also caused great difficulties learning and accessing this functionality. This study examines actual Air Force pilots who were qualified on the newly introduced advanced FMS and shows that the design of the system itself is a primary source of difficulty learning the system. Twenty representative tasks were selected which the pilots could be expected to accomplish on an ' actual flight. These tasks were analyzed using the RAFIV stage model (Sherry, Polson, et al. 2002). This analysis demonstrates that a great burden is placed on remembering complex reformulation of the task to function mapping. 65% of the tasks required retaining one access steps in memory to accomplish the task, 20% required two memorized access steps, and 15% required zero memorized access steps. The probability that a participant would make an access error on the tasks was: two memorized access steps - 74%, one memorized access step - 13%, and zero memorized access steps - 6%. Other factors were analyzed as well, including experience with the system and frequency of use. This completed the picture of a system with many memorized steps causing difficulty with the new system, especially when trying to fine where to access the correct function.

Fennell, Karl↗

NASA NTRS1979

Logarithmic spiral grids for image processing

A picture digitization grid based on logarithmic spirals rather than Cartesian coordinates is presented. Expressing this curvilinear grid as a conformal exponential mapping reveals useful image processing properties. The mapping induces a computational simplification that suggests parallel architectures in which most geometric transformations are effected by data shifting in memory rather than arithmetic on coordinates. These include fast, parallel noise-free rotation, scaling, and some projective transformations of pixel defined images. Conformality of the mapping preserves local picture-processing operations such as edge detection.

Weiman, C. F. R.↗

NASA NTRS2002

Spatial learning and memory is preserved in rats after early development in a microgravity environment

This study evaluated the cognitive mapping abilities of rats that spent part of their early development in a microgravity environment. Litters of male and female Sprague-Dawley rat pups were launched into space aboard the National Aeronautics and Space Administration space shuttle Columbia on postnatal day 8 or 14 and remained in space for 16 days. These animals were designated as FLT groups. Two age-matched control groups remained on Earth: those in standard vivarium housing (VIV) and those in housing identical to that aboard the shuttle (AGC). On return to Earth, animals were tested in three different tasks that measure spatial learning ability, the Morris water maze (MWM), and a modified version of the radial arm maze (RAM). Animals were also tested in an open field apparatus to measure general activity and exploratory activity. Performance and search strategies were evaluated in each of these tasks using an automated tracking system. Despite the dramatic differences in early experience, there were remarkably few differences between the FLT groups and their Earth-bound controls in these tasks. FLT animals learned the MWM and RAM as quickly as did controls. Evaluation of search patterns suggested subtle differences in patterns of exploration and in the strategies used to solve the tasks during the first few days of testing, but these differences normalized rapidly. Together, these data suggest that development in an environment without gravity has minimal long-term impact on spatial learning and memory abilities. Any differences due to development in microgravity are quickly reversed after return to earth normal gravity.

NASA Discipline Neuroscience↗

DOE OSTI2022

Map reduce using coordination namespace hardware acceleration

A system and method for supporting data MapReduce operations in a tuple space/coordinated namespace (CNS) extended memory storage architecture. The system-wide CNS provides an efficient means for storing and communicating data generated by local processes running at the nodes, and coordinated to provide MapReduce operations in a multi-nodal system. A hardware accelerated mechanism supports map reduce sorting/shuffle operations and reduce operations according to an aggregate function. Local processes running at a node generate a tuple corresponding to data generated by a process, each tuple having a tuple name and tuple data value corresponding to the generated data. Each tuple is processed and stored at the node or another node, dependent upon its tuple name. Tuple records associated with a tuple name are accumulated at one or more nodes according to a linked list structure at each that is accessible via a hash table index pointer at the node.

Jacob, Philip↗

NASA NTRSDate not supplied

A Machine Learning Ready Dataset of Acoustic Power Maps for Detection of Active Region Emergence

The development of an accurate forecast for solar eruptive activity has become increasingly important in order to prevent any potential impact on activities in space and the Earth's environment. It is therefore crucial to detect active regions before they appear on the solar surface and create early warning capabilities for upcoming Space Weather disturbances. In this work, 9TB of solar data (SDO/HMI dopplergrams, magnetograms and continuum intensity maps) involving the emergence of 61 NOAA solar active regions since 2010 were processed using the NASA HECC capabilities. An acoustic power maps time-series dataset was created (for four different frequency ranges and processed to take into account the solar sphere geometric effect ) which can be used for understanding the dynamics of the solar surface and train a variety of ML models. The calculated acoustic power maps carry precursor information associated with the decrease in continuum intensity on the solar surface, verifying older helioseismology research. Our results show that a Long Short-Term Memory (LSTMs) model, with a modest layer depth and the right hyperparameters tuned, when trained on this solar acoustic power maps dataset can predict without false negatives a drop in intensity (associated with the emergence of the active region), up to 18 hours in advance.

SMD↗

NASA NTRS1989

NETRA: A parallel architecture for integrated vision systems 2: Algorithms and performance evaluation

In part 1 architecture of NETRA is presented. A performance evaluation of NETRA using several common vision algorithms is also presented. Performance of algorithms when they are mapped on one cluster is described. It is shown that SIMD, MIMD, and systolic algorithms can be easily mapped onto processor clusters, and almost linear speedups are possible. For some algorithms, analytical performance results are compared with implementation performance results. It is observed that the analysis is very accurate. Performance analysis of parallel algorithms when mapped across clusters is presented. Mappings across clusters illustrate the importance and use of shared as well as distributed memory in achieving high performance. The parameters for evaluation are derived from the characteristics of the parallel algorithms, and these parameters are used to evaluate the alternative communication strategies in NETRA. Furthermore, the effect of communication interference from other processors in the system on the execution of an algorithm is studied. Using the analysis, performance of many algorithms with different characteristics is presented. It is observed that if communication speeds are matched with the computation speeds, good speedups are possible when algorithms are mapped across clusters.

Choudhary, Alok N.↗

DOE OSTI2023

Brief Announcement: Communication Optimal Sparse LU Factorization for Planar Matrices

We introduce a new parallel algorithm for solving sparse LU factorization of planar matrices, which commonly arise in the finite element method for 2D PDEs. Existing scalable methods, such as the multifrontal approach with subtree-to-subcube mapping by Gupta et al. [1] and right-looking with 3D mapping by Sao et al. [2] fail to achieve optimal communication costs for these matrices. Our new algorithm combines 3D mapping and subtree-to-subcube mapping to minimize communication costs while allowing trade-offs between extra memory and reduced communication. We demonstrate that our proposed algorithm attains the communication lower bound up to a factor of O(log log n) in the memory-optimal case and up to a factor of O(log P) in the memory-independent case for an n-dimensional planar sparse matrix on P processors.

Sao, Piyush↗

NASA NTRS1995

Comprehension and retrieval of failure cases in airborne observatories

This paper describes research dealing with the computational problem of analyzing and repairing failures of electronic and mechanical systems of telescopes in NASA's airborne observatories, such as KAO (Kuiper Airborne Observatory) and SOFIA (Stratospheric Observatory for Infrared Astronomy). The research has resulted in the development of an experimental system that acquires knowledge of failure analysis from input text, and answers questions regarding failure detection and correction. The system's design builds upon previous work on text comprehension and question answering, including: knowledge representation for conceptual analysis of failure descriptions, strategies for mapping natural language into conceptual representations, case-based reasoning strategies for memory organization and indexing, and strategies for memory search and retrieval. These techniques have been combined into a model that accounts for: (a) how to build a knowledge base of system failures and repair procedures from descriptions that appear in telescope-operators' logbooks and FMEA (failure modes and effects analysis) manuals; and (b) how to use that knowledge base to search and retrieve answers to questions about causes and effects of failures, as well as diagnosis and repair procedures. This model has been implemented in FANSYS (Failure ANalysis SYStem), a prototype text comprehension and question answering program for failure analysis.

Alvarado, Sergio J.↗

DOE OSTI2022

Optical quantum memory for noble-gas spins based on spin-exchange collisions

Optical quantum memories, which store and preserve the quantum state of photons, rely on a coherent mapping of the photonic state onto matter states that are optically accessible. Here we outline and characterize schemes to map the state of photons onto long-lived but optically inaccessible collective states of noble-gas spins. The mapping employs coherent spin-exchange interaction arising from random collisions with alkali vapor. We propose efficient storage strategies in two operating regimes and analyze their performance for several proposed experimental configurations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2010

Systems, methods, and products for graphically illustrating and controlling a droplet actuator

Systems for controlling a droplet microactuator are provided. According to one embodiment, a system is provided and includes a controller, a droplet microactuator electronically coupled to the controller, and a display device displaying a user interface electronically coupled to the controller, wherein the system is programmed and configured to permit a user to effect a droplet manipulation by interacting with the user interface. According to another embodiment, a system is provided and includes a processor, a display device electronically coupled to the processor, and software loaded and/or stored in a storage device electronically coupled to the controller, a memory device electronically coupled to the controller, and/or the controller and programmed to display an interactive map of a droplet microactuator. According to yet another embodiment, a system is provided and includes a controller, a droplet microactuator electronically coupled to the controller, a display device displaying a user interface electronically coupled to the controller, and software for executing a protocol loaded and/or stored in a storage device electronically coupled to the controller, a memory device electronically coupled to the controller, and/or the controller.

Paik, Philip Y.↗