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Deep Modeling: Circuit Characterization Using Theory Based Models in a Data Driven Framework
Analog computational circuits have been demonstrated to provide substantial improvements in power and speed relative to digital circuits, especially for applications requiring extreme parallelism but only modest precision. Deep machine learning is one such area and stands to benefit greatly from analog and mixed-signal implementations. However, even at modest precisions, offsets and non-linearity can degrade system performance. Furthermore, in all but the simplest systems, it is impossible to directly measure the intermediate outputs of all sub-circuits. The result is that circuit designers are unable to accurately evaluate the non-idealities of computational circuits in-situ and are therefore unable to fully utilize measurement results to improve future designs. In this paper we present a technique to use deep learning frameworks to model physical systems. Recently developed libraries like TensorFlow make it possible to use back propagation to learn parameters in the context of modeling circuit behavior. Offsets and scaling errors can be discovered even for sub-circuits that are deeply embedded in a computational system and not directly observable. The learned parameters can be used to refine simulation methods or to identify appropriate compensation strategies. We demonstrate the framework using a mixed-signal convolution operator as an example circuit.
A Data-Driven Approach to PDE-Constrained Optimization under Uncertainty.
Abstract not provided.
Data Driven Camera Distortion Correction Model Including Effects from Temperature Variation and Diffraction.
Abstract not provided.
Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow.
Abstract not provided.
A Data-Driven Approach to PDE-Constrained Optimization under Uncertainty.
Abstract not provided.
A Data-Driven Approach to PDE-Constrained Optimization under Uncertainty.
Abstract not provided.
The Emerging Cyber-Physical Threat: A Data-Driven Analysis of Major Cyber-Physical Attacks from Around the World.
Abstract not provided.
Time will tell: A longitudinal data-driven investigation of brain-behavior relationships during reading development.
Abstract not provided.
Data Driven Camera Distortion Correction Model Including Effects from Temperature Variation and Diffraction.
Abstract not provided.
A Data-Driven Approach to PDE-Constrained Optimization under Uncertainty.
Abstract not provided.
Security Vulnerability and Patch Management in Electric Utilities: A Data-Driven Analysis
This paper explores a real security vulnerability and patch management dataset from an electric utility in order to shed light on characteristics of the vulnerabilities that electric utility assets have and how they are remediated in practice. Specifically, it first analyzes the distribution of vulnerabilities over software, assets, and other metric. Then it analyzes how vulnerability features affect remediate actions.
PADDLE: Performance Analysis using a Data-driven Learning Environment
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Data-Driven Bayesian Model Selection: Parameter Space Dimension Reduction using Automatic Relevance Determination Priors.
Abstract not provided.
Data-Driven Uncertainty Quantification for Remote Sensing.
Abstract not provided.
A Data-Driven Approach to PDE-Constrained Optimization under Uncertainty.
Abstract not provided.
Using Data-Driven Uncertainty Quantification to Support Decision Making.
Abstract not provided.