scholarly journals SRAM-Based PUF Reliability Prediction Using Cell-Imbalance Characterization in the State Space Diagram

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 135
Author(s):  
Gabriel Torrens ◽  
Abdel Alheyasat ◽  
Bartomeu Alorda ◽  
Sebastià A. Bota
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
State Space ◽  
Good Predictor ◽  
Logistic Function ◽  
Reliability Estimation ◽  
Reliability Prediction ◽  
Start Up ◽  
Two Component ◽  
Early Design Stages

This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternative parameter defined as the distance between the origin and the separatrix in the bit-cell state space to quantify the mismatch of the cell. The resulting distribution of this parameter obtained from Monte Carlo simulations is then related to the start-up probability distribution using a two-component logistic function. The reported results show that the proposed imbalance factor is a good predictor for PUF-related reliability estimation with the advantage that can be applied at the early design stages.

scholarly journals To BAN or Not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection

2021 ◽  
Author(s):  
Kristian Miok ◽  
Blaž Škrlj ◽  
Daniela Zaharie ◽  
Marko Robnik-Šikonja
Keyword(s):  
Monte Carlo ◽  
Hate Speech ◽  
Classification Performance ◽  
Reliability Estimation ◽  
Superior Performance ◽  
Speech Detection ◽  
Attention Networks ◽  
Reliability Estimates ◽  
Viable Mechanism ◽  
Affective Dimensions

AbstractHate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the results of the proposed approach on hate speech detection problems in several languages. Additionally, we test whether affective dimensions can enhance the information extracted by the BERT model in hate speech classification. Our experiments show that Monte Carlo dropout provides a viable mechanism for reliability estimation in transformer networks. Used within the BERT model, it offers state-of-the-art classification performance and can detect less trusted predictions.

Monte Carlo Simulation of Electronic Excitation Migrationand Trappingin Disordered Two-Component Systems. A Comparison with Analytical Theories

1989 ◽  
Vol 44 (4) ◽  
pp. 257-261 ◽  
Author(s):  
Sławomir Błonski ◽  
Czesław Bojarski
Keyword(s):  
Monte Carlo ◽  
Electronic Excitation ◽  
Two Component System ◽  
Component System ◽  
Steady State Fluorescence ◽  
Donor And Acceptor ◽  
Component Systems ◽  
Two Component ◽  
Two Component Systems ◽  
Viscous Solution

Abstract Monte Carlo simulations of quantum yield and anisotropy of fluorescence in two-component systems have been conducted with various donor and acceptor concentrations and Förster radii ratios RDAO/RDDO. The influence of excitation migration and trapping on the fluorescence of the viscous solution has been considered. The results of the simulations have shown that steady-state fluorescence of a two-component system depends on the RDAO/RDDO ratio as predicted in LAF theory.

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