Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “out of memory”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An organic electrochemical transistor for multi-modal sensing, memory and processing

By integrating sensing, memory and processing functionalities, biological nervous systems are energy and area efficient. Emulating such capabilities in artificial systems is, however, challenging and is limited by the device heterogeneity of sensing and processing cores. Here we report an organic electrochemical transistor capable of sensing, memory and processing. The device has a vertical traverse architecture and a crystalline–amorphous channel that can be selectively doped by ions to enable two reconfigurable modes: a volatile receptor and a non-volatile synapse. As a volatile receptor, the device is capable of multi-modal sensing and is responsive to stimuli such as ions and light. As a non-volatile synapse, it is capable of 10-bit analogue states, low switching stochasticity and good state retention. We also show that the homogeneous integration of the devices could provide functions such as conditioned reflexes and could be used for real-time cardiac disease diagnoses via reservoir computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Inductance meets memory in a quantum magnet

Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn₃Si₂Te₆, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.

Cao, Tristan [University of Colorado, Boulder]↗
DOE OSTI2024

Energy efficient photonic memory based on electrically programmable embedded III-V/Si memristors: switches and filters

Abstract Over the past few years, extensive work on optical neural networks has been investigated in hopes of achieving orders of magnitude improvement in energy efficiency and compute density via all-optical matrix-vector multiplication. However, these solutions are limited by a lack of high-speed power power-efficient phase tuners, on-chip non-volatile memory, and a proper material platform that can heterogeneously integrate all the necessary components needed onto a single chip. We address these issues by demonstrating embedded multi-layer HfO 2 /Al 2 O 3 memristors with III-V/Si photonics which facilitate non-volatile optical functionality for a variety of devices such as Mach-Zehnder Interferometers, and (de-)interleaver filters. The Mach-Zehnder optical memristor exhibits non-volatile optical phase shifts > π with ~33 dB signal extinction while consuming 0 electrical power consumption. We demonstrate 6 non-volatile states each capable of 4 Gbps modulation. (De-) interleaver filters were demonstrated to exhibit memristive non-volatile passband transformation with full set/reset states. Time duration tests were performed on all devices and indicated non-volatility up to 24 hours and beyond. We demonstrate non-volatile III-V/Si optical memristors with large electric-field driven phase shifts and reconfigurable filters with true 0 static power consumption. As a result, co-integrated photonic memristors offer a pathway for in-memory optical computing and large-scale non-volatile photonic circuits.

Cheung, Stanley (ORCID:0000000248860013)↗
DOE OSTI2020

Bonding similarities and differences between Y–Sb–Te and Sc–Sb–Te phase-change memory materials

The scandium (Sc) – alloyed Sb 2 Te 3 phase-change alloy has recently been found to enable ultrafast crystal nucleation due to the formation of Sc-stabilized octahedral motifs in the amorphous phase, rendering cache-type phase-change memory feasible. When yttrium (Y) is added, however, non-octahedral bonding patterns form in the amorphous Sb 2 Te 3 -based network even though Y has a valence electron configuration similar to that of Sc and also forms perfect octahedral bonding environments with tellurium in the YTe crystal. Here we elucidate the origin of this difference between Sc and Y, by carrying out thorough ab initio simulations and orbital-based bonding analyses on amorphous Y–Sb–Te and Sc–Sb–Te compounds. We also demonstrate how the smooth overlap of atomic positions (SOAP) similarity kernel can be used to quantify the structural similarity of local motifs in the amorphous phase with respect to various crystalline yttrium and scandium tellurides, both in the nearest-neighbor shell and beyond. We discover that the bonding contrast of Y- and Sc-centered structural motifs in amorphous Sb 2 Te 3 stems from their parent crystals at high Te concentrations. The larger atomic radius of Y and the weaker charge transfer when bonded with Te is found to allow more Te neighbors and cause a more open bonding environment, leading to higher coordination numbers and non-octahedral environments. We discuss the implications of the different local environments for practical applications in memory devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Phase control of coherent acoustic phonons in gold bipyramids for optical memory and manipulating plasmon–exciton coupling

Recent efforts have targeted manipulation of nanomaterial vibrational modes in applications such as chemical/mass sensing, optical switching, and phonon-driven photochemistry. While impulsive photoexcitation can generate coherent phonons, multiple excitation pulses offer the prospect of control and manipulation of coherent phonon modes for functions of optical memory and logic. In this work, we use such an approach to inject an arbitrary coherent phonon phase into a colloidal ensemble of highly monodisperse gold bipyramids. We then demonstrate that this technique can be applied to a system that exhibits plasmon-exciton coupling to further manipulate the hybridization of the system. This ability to manipulate acoustic phonons and hybridization can enable optical logic applications of acoustic phonons in addition to optical memory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A nanocryotron memory and logic family

The development of superconducting electronics based on nanocryotrons has been limited so far to few device circuits, in part due to the lack of standard and robust logic cells. Here, we introduce and experimentally demonstrate designs for a set of nanocryotron-based building blocks that can be configured and combined to implement memory and logic functions. The devices were fabricated by patterning a single superconducting layer of niobium nitride and measured in liquid helium on a wide range of operating points. The tests show 10 - 4 bit error rates with above ± 20 % margins up to 50 MHz and the possibility of operating under the effect of an out-of-plane 36 mT magnetic field, with ± 30 % margins at 10 MHz. Additionally, we designed and measured an equivalent delay-flip-flop made of two memory cells to show the possibility of combining multiple building blocks to make larger circuits. These blocks may constitute a solid foundation for the development of nanocryotron logic circuits and finite-state machines with potential applications in the integrated processing and control of superconducting nanowire single-photon detectors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A parallel, distributed memory implementation of the adaptive sampling configuration interaction method

The many-body simulation of quantum systems is an active field of research that involves several different methods targeting various computing platforms. Many methods commonly employed, particularly coupled cluster methods, have been adapted to leverage the latest advances in modern high-performance computing. Selected configuration interaction (sCI) methods have seen extensive usage and development in recent years. However, the development of sCI methods targeting massively parallel resources has been explored only in a few research works. Here, we present a parallel, distributed memory implementation of the adaptive sampling configuration interaction approach (ASCI) for sCI. In particular, we will address the key concerns pertaining to the parallelization of the determinant search and selection, Hamiltonian formation, and the variational eigenvalue calculation for the ASCI method. Load balancing in the search step is achieved through the application of memory-efficient determinant constraints originally developed for the ASCI-PT2 method. The presented benchmarks demonstrate near optimal speedup for ASCI calculations of Cr 2 (24e, 30o) with 10 6 , 10 7 , and 3 × 10 8 variational determinants on up to 16 384 CPUs. Importantly, to the best of the authors’ knowledge, this is the largest variational ASCI calculation to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Characterizing Tradeoffs in Memory, Accuracy, and Speed for Chemistry Tabulation Techniques

Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. Here, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. Furthermore we demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multimeric antibodies from antigen-specific human IgM+ memory B cells restrict Plasmodium parasites

Multimeric immunoglobulin-like molecules arose early in vertebrate evolution, yet the unique contributions of multimeric IgM antibodies to infection control are not well understood. This is partially due to the difficulty of distinguishing low-affinity IgM, secreted rapidly by plasmablasts, from high-affinity antibodies derived from later-arising memory cells. We developed a pipeline to express B cell receptors (BCRs) from Plasmodium falciparum–specific IgM+ and IgG+ human memory B cells (MBCs) as both IgM and IgG molecules. BCRs from both subsets were somatically hypermutated and exhibited comparable monomeric affinity. Crystallization of one IgM+ MBC-derived antibody complexed with antigen defined a linear epitope within a conserved Plasmodium protein. In its physiological multimeric state, this antibody displayed exponentially higher antigen binding than a clonally identical IgG monomer, and more effectively inhibited P. falciparum invasion. Forced multimerization of this IgG significantly improved both antigen binding and parasite restriction, underscoring how avidity can alter antibody function. This work demonstrates the potential of high-avidity IgM in both therapeutics and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A passion for theoretical physics: a special issue in memory of Peter G O Freund

This is a preface to A Passion for Theoretical Physics, a special issue collection of articles published in J. Phys. A in memory of Peter G. O. Freund. We dedicate this special issue to the memory of our esteemed colleague, inspiring teacher and cherished friend, Peter Freund. Peter George Oliver Freund was born on 7 September 1936 in Timisoara, Romania. He obtained his PhD under the supervision of Walter Thirring at the University of Vienna in 1960. He came to the University of Chicago in 1963, where he remained throughout his career. He passed away on 6 March 2018.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Gravitational memory and Ward identities in the local detector frame

Gravitational memory, which describes the permanent shift in the strain after the passage of gravitational waves, is directly related to Weinberg’s soft graviton theorems and the Bondi-Metzner-Sachs (BMS) symmetry group of asymptotically flat space-times. In this work, we provide an equivalent description of the phenomenon in local coordinates around gravitational wave detectors, such as transverse-traceless (TT) gauge. We show that gravitational memory is encoded in large residual diffeomorphisms in this gauge, which include time-dependent anisotropic spatial rescalings, and prove their equivalence to BMS transformations when translated to TT gauge. We then derive the associated Ward identities and associated soft theorems, for both scattering amplitudes and equal-time (in-in) correlation functions, and explicitly check their validity for planar gravitational waves. Furthermore, the in-in identities are recognized as the flat-space analog of the well-known inflationary consistency relations.

General relativity↗
DOE OSTI2025

Learning efficient erasure protocols for an underdamped memory

Here we apply evolutionary reinforcement learning to a simulation model to identify efficient time-dependent erasure protocols for a physical realization of a 1-bit memory using an underdamped mechanical cantilever. We show that these protocols, when applied to the cantilever in the laboratory, are considerably more efficient than our best hand-designed protocols. The learned protocols allow reliable high-speed erasure by minimizing the heating of the memory during its operation. More generally, the combination of methods used here opens the door to the rational design of efficient protocols for various physics applications.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Topological spin memory of antiferromagnetically coupled skyrmion pairs in Co/Gd/Pt multilayers

Antiferromagnetically (AFM) coupled skyrmions offer potential advantages for spintronic devices, including reduced dipolar fields that may enable smaller skyrmion sizes and a reduction of the skyrmion Hall effect. However, the topological stability of AFM-coupled skyrmions subjected to dramatic spin deformation through low-temperature cycling has not been investigated. We report the discovery of a topological spin memory effect for AFM-coupled skyrmion pairs in [Co/Gd/Pt](10) multilayered films. Photoemission electron microscopy imaging shows that bubble skyrmions in the multilayer that are stable at room temperature evolve into complex in-plane spin textures as the temperature is lowered and reform completely when the sample is warmed back up. Simulations demonstrate that Dzyaloshinskii-Moriya interactions play a key role in this spin memory effect, and furthermore reveal that the topological charge is preserved throughout the dramatic spin texture rearrangement and recovery. These results highlight a key aspect of topological protection-the preservation of the topological properties under continuous deformation-and also provide a promising avenue for information encryption and recovery.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Kondo enabled transmutation between spinons and superconducting vortices: Origin of magnetic memory in 4 H b − TaS 2

Recent experiments [Persky , ] demonstrate a magnetic memory effect in 4 H b − TaS 2 above its superconducting transition temperature, where Abriokosov vortices are spontaneously generated by lowering the temperature at zero magnetic field after field training the normal state. Motivated by the experiment, we propose the chiral quantum spin liquid (QSL) stabilized in the constituent layers of 4 H b − TaS 2 as a mechanism. We model 4 H b − TaS 2 as coupled layers of the chiral QSL and the superconductor. Through the Kondo coupling between the localized moments and conduction electrons, there is mutual transmutation between spinons and vortices during the thermal-cycling process, which yields the magnetic memory effect observed in experiments. We also propose a mechanism to stabilize chiral and nematic superconductivity in 4 H b − TaS 2 through the Kondo coupling of conduction electrons to the chiral QSL. Our picture suggests 4 H b − TaS 2 as an exciting platform to explore the interplay between QSL and superconductivity through the Kondo effect. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning

Terrestrial ecosystems play a central role in the global carbon cycle and affect climate change. However, our predictive understanding of these systems is still limited due to their complexity and uncertainty about how key drivers and their legacy effects influence carbon fluxes. Here, we propose an interpretable Long Short-Term Memory (iLSTM) network for predicting net ecosystem CO 2 exchange (NEE) and interpreting the influence on the NEE prediction from environmental drivers and their memory effects. We consider five drivers and apply the method to three forest sites in the United States. Besides performing the prediction in each site, we also conduct transfer learning by using the iLSTM model trained in one site to predict at other sites. Results show that the iLSTM model produces good NEE predictions for all three sites and, more importantly, it provides reasonable interpretations on the input driver's importance as well as their temporal importance on the NEE prediction. Additionally, the iLSTM model demonstrates good across-site transferability in terms of both prediction accuracy and interpretability. The transferability can improve the NEE prediction in unobserved forest sites, and the interpretability advances our predictive understanding and guides process-based model development.

Liu, Siyan↗
DOE OSTI2020

Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization

We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy. The first algorithm is a parallelization of an existing sequential approach that uses solvers for non symmetric NMF. The second algorithm is a novel approach based on the Gauss-Newton method. It exploits second-order information without incurring large computational and memory costs. We evaluate the scalability of our algorithms on the Summit system at Oak Ridge National Laboratory, scaling up to 128 nodes (4,096 cores) with 70% efficiency. Additionally, we demonstrate our software on an image segmentation task.

Eswar, Srinivas↗