Memristor-based in-memory processor for high precision semantic text classification

Text classification is an important component of digital media such as natural language processing, image labeling, sentiment analysis, spam filtering, chatbots, and translators. In this work, effort was devoted to develop an in-memory processor for Bayesian text classification using memristive crossbar architecture, in which memristive switches were employed to store information required for the classification of text. The efficacy of the proposed circuit was tested on two distinct datasets consisting of a total of 55,575 texts. The circuit was found to be efficient to categorize the texts with an average accuracy of 91%. This work paves the way for hardware realization of cognitive systems using in-memory processors.

Automated summary

Effort was devoted to develop an in-memory processor for Bayesian text classification using memristive crossbar architecture, which proved to be efficient with an average accuracy of 91% on two distinct datasets. This work paves the way for hardware realization of cognitive systems using in-memory processors.