A Statistical Methodology for Post-Fabrication Weight Tuning in a Binary Perceptron

Elham Azari, Ankit Wagle, Sunil Khatri, Sarma Vrudhula

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present an efficient statistical approach for analyzing the robustness of threshold logic gates in the presence of process variations and using the results of that analysis to tune the weights of the threshold gate to maximize yield. Although the proposed methodology is completely general and can be applied to any circuit (digital, mixed-signal or analog), we demonstrate it using the flash transistor based threshold logic gate reported in [1]. The statistical approach presented in this paper involves construction of an efficient database and the design of a stochastic simulator based on an extension to the polynomial chaos technique. The results demonstrate that this methodology when using the stochastic simulator achieves a maximum speed up of 56.5X in reducing the number of HSPICE iterations without loss of accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st International Symposium on Quality Electronic Design, ISQED 2020
PublisherIEEE Computer Society
Pages141-148
Number of pages8
ISBN (Electronic)9781728142074
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event21st International Symposium on Quality Electronic Design, ISQED 2020 - Santa Clara, United States
Duration: Mar 25 2020Mar 26 2020

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2020-March
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference21st International Symposium on Quality Electronic Design, ISQED 2020
CountryUnited States
CitySanta Clara
Period3/25/203/26/20

Keywords

  • arbitrary polynomial chaos
  • binary perceptron
  • process variation
  • stochastic simulator

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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