Deep Learning Seismic Signal Detection on the International Monitoring System
Abstract not provided.
Publications
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Jump to search filtersIntegrated Photonics and Vacuum Package Development for a Cold-Atom Interferometer
Abstract not provided.
A Matrix-Free Approach for Algebraic Multigrid
Abstract not provided.
Inverse Methods - Users Manual 5.6
The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.
A Quantum Advantage for a Natural Streaming Problem
Abstract not provided.
Error-in-variables modelling for operator learning
Abstract not provided.
Fatigue and fracture of pipeline steels in high-pressure hydrogen gas (PVP2022-84757)
Abstract not provided.
X-ray Heating and Temperature in Multielement Laboratory Photoionized Plasmas at Z
Abstract not provided.
Review: Mechanism of Low-Temperature Decomposition of Ammonium Perchlorate
Abstract not provided.
Explainable AI in Cybersecurity Operations: Lessons Learned from xAI Tool Deployment
Abstract not provided.
Evolving to Data Science or How to Succeed at Sandia without doing Scientific Computing
Abstract not provided.
Nuclear Energy Fuel Cycle (NEFC) Knowledge Management Project
Abstract not provided.
Improving Digital Twins by Learning from a Fleet of Assets
Abstract not provided.
Reinforcement Learning for PDE Control Problems
Abstract not provided.
Comparing Instrumentation Selection Techniques for Vibration Testing
Abstract not provided.
Energy Storage Management System (ESMS) and Evaluation
Abstract not provided.
Lifetime Characterization Methods in InAs/InAsSb type-II superlattice
Abstract not provided.
Structural metamaterials
Abstract not provided.
Bayesian Calibration of Interatomic Potential Models for Binary Alloys
Abstract not provided.
In Search of Resilient Chemical Technologies with Minimum Carbon Intensity
Abstract not provided.
Expansion and Transferability of Seismic Deep CNN Denoiser to Global Networks
Abstract not provided.
Crossroads: A Status on Design, Deployment, Acceptance, and Operation
Abstract not provided.
Multi-Layered Video Authentication for Treaty Inspection Regimes (1 minute summary)
Abstract not provided.
DECOVALEX 2023 TASK G: Step2 and Step 3 - Benchmark Exercises: G2-HM-BE-2D and G3-TM-BE-2DSNL Modeling Progress
Abstract not provided.
An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory
IEEE Transactions on Circuits and Systems I: Regular Papers
We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.
Results 7476–7500 of 99,299