On-chip face recognition system design with memristive Hierarchical Temporal Memory

Timur Ibrayev, Ulan Myrzakhan, Olga Krestinskaya, Aidana Irmanova, Alex Pappachen James

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

20 Scopus citations


Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of the neocortex, part of the human brain, responsible for learning, classification, and making predictions. Although many works illustrate its effectiveness as a software algorithm, hardware design for HTM remains an open research problem. Hence, this work proposes an architecture for HTM Spatial Pooler and Temporal Memory with learning mechanism, which creates a single image for each class based on important and unimportant features of all images in the training set. In turn, the reduction in the number of templates within database reduces the memory requirements and increases the processing speed. Moreover, face recognition analysis indicates that for a large number of training images, the proposed design provides higher accuracy results (83.5%) compared to only Spatial Pooler design presented in the previous works.
Original languageEnglish (US)
Title of host publicationJournal of Intelligent and Fuzzy Systems
PublisherIOS PressNieuwe Hemweg 6BAmsterdam1013 BG
Number of pages10
StatePublished - Jan 1 2018
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23


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