Radiation Effects in Advanced and Emerging Nonvolatile Memories.
Abstract not provided.
Marinella, Matthew J↗
SEARCH · Search NASA
Results for “Memory effect”
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.
Genetic background influences the effect of thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice
Growing evidence indicates that thirdhand smoke (THS) exposure induces many adverse health effects. However, it is unclear how THS exposure affects behavior and how host genetic background modulates phenotypic changes. Here we used the Collaborative Cross (CC) mouse population-based model to assess behavioral alterations immediately after THS exposure from 4 to 9 weeks of age. We first measured anxiety-like behavior in six strains using light/dark box combined with a custom multivariate mouse tracking system. We developed an anxiety risk scoring system based on anxiety-related traits and then evaluated the THS impact on them. THS exposure significantly decreased anxiety risk in CC019 ( P = 0.002) and CC051 ( P = 0.009), but increased anxiety risk in CC036 ( P < 0.001), while the other three strains did not show significant changes in anxiety-related traits. Such differences were driven by female mice for the six measures of anxiety-like behavior. Memory potential was measured in the same cohort of mice using the passive avoidance assay. Both THS-exposed male and female CC019 mice displayed significant memory loss compared to controls while no significant changes were found in the other five strains. This study provides strong evidence that THS exposure leads to strain-dependent changes in anxiety-like behavior and memory, suggesting that host genetic variations play a critical role in individual susceptibility to THS-induced effects.
59 BASIC BIOLOGICAL SCIENCES↗
Performance of CUDA Unified Memory in CMS Heterogeneous Pixel Reconstruction
The management of separate memory spaces of CPUs and GPUs brings an additional burden to the development of software for GPUs. To help with this, CUDA unified memory provides a single address space that can be accessed from both CPU and GPU. The automatic data transfer mechanism is based on page faults generated by the memory accesses. This mechanism has a performance cost, that can be with explicit memory prefetch requests. Various hints on the inteded usage of the memory regions can also be given to further improve the performance. The overall effect of unified memory compared to an explicit memory management can depend heavily on the application. In this paper we evaluate the performance impact of CUDA unified memory using the heterogeneous pixel reconstruction code from the CMS experiment as a realistic use case of a GPU-targeting HEP reconstruction software. We also compare the programming model using CUDA unified memory to the explicit management of separate CPU and GPU memory spaces.
Kortelainen, Matti J.↗
Effect of directed aging on nonlinear elasticity and memory formation in a material
Disordered solids often change their elastic response as they slowly age. Using experiments and simulations, we study how aging disordered planar networks under an applied stress affects their nonlinear elastic response. We are able to modify dramatically the elastic properties of our systems in the nonlinear regime. Using simulations, we study two models for the microscopic evolution of properties of such a material; the first considers changes in the material strength, while the second considers distortions in the microscopic geometry. Both models capture different aspects of the experiments including the encoding of memories of the aging history of the system and the dramatic effects on the material’s nonlinear elastic properties. Our results demonstrate how aging can be used to create complex elastic behavior in the nonlinear regime.
36 MATERIALS SCIENCE↗
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↗
On the Feasibility of 1T Ferroelectric FET Memory Array
To fully exploit the ferroelectric field effect transistor (FeFET) as compact embedded nonvolatile memory for various computing and storage applications, it is desirable to use a single FeFET (1T) as a unit cell and arrange the cells into an array. However, many write mechanisms for an 1T FeFET array reported in the literature are yet to be validated experimentally. In this work, we performed a comprehensive experimental characterization on the write operations in an 1T- NOR and 1T- AND array using n-channel bulk FeFETs. We discovered that: 1) the source/drain contact can only supply minority carriers (i.e., electrons) to the channel for polarization screening during the low- V TH state programming; 2) the body contact can not only supply majority carriers (i.e., holes) for efficient high- V TH state write, but also depletion charge for low- V TH state programming, though with lower efficiency; and 3) during the low/high- V TH programming, only the path that can supply negative/positive screening charges, respectively, need to respond, which necessitates the application of proper write biases on the corresponding terminal. Based on the understanding of these write mechanisms, we show the importance of localized body contact or column-wise body contact for the successful high- V TH state programming. We also show that the previously proposed C- AND write scheme fails to program the FeFET to the low- V TH state for our devices. Finally, we propose several write schemes for both 1T- AND and 1T- NOR arrays for various scenarios providing insights for choosing the appropriate write scheme, which will facilitate the adoption of 1T FeFET memory arrays for emerging applications.
FeFET↗
Topological insulator-based high efficiency switching of magnetic unit, method and applications
A magneto-electronic device may include: a spin-orbit torque (SOT) generator layer; a magnetic memory layer; and/or sensing electrodes configured to measure a Hall effect of the magnetic memory layer. The SOT generator layer may include topological insulator material, and the magnetic memory layer may include ferromagnetic material with perpendicular magnetic anisotropy. A magneto-electronic device may include: a spin-orbit torque (SOT) generator layer; a first magnetic memory layer on the SOT generator layer; an insulating layer on the first magnetic memory layer; and/or a second magnetic memory layer on the insulating layer. The SOT generator layer may include topological insulator material. The first magnetic memory layer and the second magnetic memory layer may include ferromagnetic material with either perpendicular magnetic anisotropy or in-plane magnetic anisotropy.
36 MATERIALS SCIENCE↗
Efficient GPU Implementation of Automatic Differentiation for Computational Fluid Dynamics
Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a forward-mode AD method based on multivariate dual numbers that addresses these challenges and simultaneously reduces the floating-point operation count. The dimension of the multivariate dual numbers is optimized for performance. The flux computations are restructured to minimize the number of temporary variables and reduce register pressure. For effective utilization of memory bandwidth, shared memory is used to store the local flux Jacobian. This AD implementation is compared with several other Jacobian implementations on an NVIDIA V100 GPU (V100). For three-dimensional perfect-gas compressible-flow equations implemented in a practical CFD code, the AD implementation of a flux Jacobian based on multivariate dual numbers of dimension 5 outperforms all other GPU AD implementations on V100. Its performance is comparable with the optimized hand-differentiated version. Finally, the implementation achieves 75% of the peak floating-point throughput and 61 % of the peak global device memory bandwidth usage.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
T-FSM: A Scalable Distributed Task-Based System for Frequent Subgraph Pattern Mining from a Big Graph
Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel and distributed systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, poor scale-out capability, and possibly inaccurate results. In this article, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. The execution engine of T-FSM supports both intra-machine parallelism and inter-machine parallelism. For intra-machine parallelism, T-FSM adopts a novel task-based execution model to ensure high multithreading concurrency, bounded memory consumption, and effective load balancing. For inter-machine parallelism, T-FSM ensures good scale-out performance with a lightweight pattern rebalancing approach that reduces workload skewness of pattern evaluations among machines. To avoid recomputing the contexts for migrated patterns, we design a novel context cache table to support concurrent and asynchronous requesting and caching of remote context data, which can timely evict and garbage collect used pattern contexts that are no longer needed to keep memory consumption bounded. Extensive experiments show that T-FSM is orders of magnitude faster than existing state-of-the-art parallel systems (more than 10×, 51×, 131×, 55× speedup over ScaleMine, DistGraph, Pangolin and Peregrine, respectively) and distributed systems (more than 42× and 88× over ScaleMine and DistGraph, respectively) for frequent subgraph pattern mining, and it scales out satisfactorily to 512 CPU cores on the Polaris supercomputer at Argonne National Laboratory.
97 MATHEMATICS AND COMPUTING↗
Low-temperature processed beta-phase In 2 Se 3 ferroelectric semiconductor thin film transistors
As scaling becomes increasingly difficult, there is growing interest in vertical or three-dimensional stacking of transistors and especially memory. Ferroelectric semiconductor field effect transistors can be key enablers to improve energy efficiency and overall chip and memory performance. In this work, low-temperature processed, back-end-of-the-line compatible transistors were demonstrated by depositing a layered chalcogenide ferroelectric semiconductor, beta-phase In 2 Se 3 , at temperature as low as 400 °C. Top gate n-channel In 2 Se 3 thin film transistors were fabricated with field-effect mobility ∼1 cm 2 V −1 s −1 , and simple polarization switching based memory results are presented.
36 MATERIALS SCIENCE↗
A Highly Effective African Swine Fever Virus Vaccine Elicits a Memory T Cell Response in Vaccinated Swine
African Swine Fever Virus (ASFV) is the causative agent of a highly contagious and lethal vector-borne disease in suids. Recently, a live attenuated virus strain, developed using the currently circulating, virulent Georgia strain (ASFV-G) with a single gene deletion (ASFV-G-ΔI177L), resulted in an effective vaccine. Nevertheless, protective immune response mechanisms induced by this candidate are poorly understood. In this study, Yorkshire crossbred swine intramuscularly vaccinated with 10 6 50% hemadsorption dose (HAD 50 ) of ASFV-G-ΔI177L or a vehicle control were challenged at 28 days post-inoculation (dpi) with 10 2 HAD 50 of ASFV-G. Analysis of purified peripheral blood mononuclear cells following inoculation and challenge revealed that CD4+, CD8+ and CD4+CD8+ central memory T cells (CD44+CD25–CD27–CD62L+CCR7+, T cm ) decreased significantly by 28 dpi in ASFV-G-ΔI177L-vaccinated swine compared to baseline and time-matched controls. Conversely, CD4+, CD8+ and CD4+CD8+ effector memory T cells (CD44+CD25–CD27–CD62–CCR7–, T em ) increased significantly among ASFV-G-ΔI177L-vaccined swine by 28 dpi compared to baseline and time-matched controls. Additionally, the percentage of natural killer (NK), CD4+ and CD4+CD8+ T em and CD8+ T cm and T em positive for IFNγ increased significantly following inoculation, surpassing that of controls by 28 dpi or earlier. These results suggest that NK and memory T cells play a role in prote
60 APPLIED LIFE SCIENCES↗
High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory
The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.
59 BASIC BIOLOGICAL SCIENCES↗
Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design
In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.
Zhang, Xingyao↗
GentenMPI: Distributed Memory Sparse Tensor Decomposition
GentenMPl is a toolkit of sparse canonical polyadic (CP) tensor decomposition algorithms that is designed to run effectively on distributed-memory high-performance computers. Its use of distributed-memory parallelism enables it to efficiently decompose tensors that are too large for a single compute node's memory. GentenMPl leverages Sandia's decades-long investment in the Trilinos solver framework for much of its parallel-computation capability. Trilinos contains numerical algorithms and linear algebra classes that have been optimized for parallel simulation of complex physical phenomena. This work applies these tools to the data science problem of sparse tensor decomposition. In this report, we describe the use of Trilinos in GentenMPl, extensions needed for sparse tensor decomposition, and implementations of the CP-ALS (CP via alternating least squares) and GCP-SGD (generalized CP via stochastic gradient descent) sparse tensor decomposition algorithms. We show that GentenMPl can decompose sparse tensors of extreme size, e.g., a 12.6-terabyte tensor on 8192 computer cores. We demonstrate that the Trilinos backbone provides good strong and weak scaling of the tensor decomposition algorithms.
97 MATHEMATICS AND COMPUTING↗
Total-Ionizing-Dose Effects on Long-term Data Retention Characteristics of Commercial 3-D NAND Memories
This article evaluates the data retention characteristics of irradiated multi-level cell (MLC) 3-D NAND flash memories. We irradiate the memory chips by a Co-60 gamma-ray source for up to 50 krad(Si) and then write a random data pattern on the irradiated chip to find its retention characteristics. The experimental results show that the data retention property of the irradiated chips is significantly degraded compared to the un-irradiated ones. We evaluate two independent strategies to improve the data retention characteristics of the irradiated chips. Furthermore, the first method involves high-temperature annealing of the irradiated chip while the second method suggests pre-programming the memory modules before deploying them in radiation-prone environments.
3-D NAND flash↗
The effect of laser scanning strategy on NiTi shape memory alloys processed by laser powder bed fusion
Not provided.
Materials Science↗
Total-Ionizing-Dose Effects on Read Noise of MLC 3D NAND Memories.
Abstract not provided.
Surendranathan, U.↗