Memristor-based in-memory processor for high precision semantic text classification. (June 2021) - Viswakumar, Aditya; Ganganaik, Priyanka B; Raj, P Michael Preetam; Rao, BVVSN Prabhakar; Kundu, Souvik
Highlights: Memristor-based in-memory processor was implemented for semantic text classification. The proposed circuit could serve as an alternative to Von Neumann based processors for large scale natural language processing. The efficacy of the proposed circuit was tested on two distinct datasets consisting of a total of 55, 575 texts. The proposed circuit was able to classify the texts with an average accuracy of 91%. Abstract: 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. Graphical abstract: Image, graphical abstract