Hierarchical Temporal Memory Features with Memristor Logic Circuits for Pattern Recognition

Olga Krestinskaya, Timur Ibrayev, Alex Pappachen James

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Hierarchical temporal memory (HTM) is a machine learning algorithm inspired by the information processing mechanisms of the human neocortex and consists of a spatial pooler (SP) and temporal memory (TM). In this paper, we develop circuits and systems to achieve the optimized design of an HTM SP, an HTM TM, and a memristive analog pattern matcher for pattern recognition applications. The HTM SP realizes an optimized hardware design through the introduction of mean overlap calculations and by replacing the threshold determination in the inhibition stage with a weighted summation operator over the neighborhood of the pixel under consideration. HTM TM is based on discrete analog memristive memory arrays and a weight update procedure. The operation of the proposed system is demonstrated for a face recognition problem, using the standard AR, ORL, and Yale databases, and for speech recognition, using the TIMIT database, with achieved accuracies of 87.21% and approximately 90%, respectively, given an SNR of 10 dB. Visual data processing using binary HTM SP features requires less storage and processing memory than required by the traditional processing methods, with the area and power requirements for its implementation being 0.096 mm2 and 1756 mW, respectively. The design of the TM circuit for a single pixel requires 23.85 μm2 of area and 442.26 μW of power.
Original languageEnglish (US)
Pages (from-to)1143-1156
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number6
StatePublished - Jun 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software
  • Electrical and Electronic Engineering


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