TY - JOUR
T1 - HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition
AU - James, Alex Pappachen
AU - Fedorova, Irina
AU - Ibrayev, Timur
AU - Kudithipudi, Dhireesha
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.
AB - Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.
UR - http://www.scopus.com/inward/record.url?scp=85016485428&partnerID=8YFLogxK
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U2 - 10.1109/TBCAS.2016.2641983
DO - 10.1109/TBCAS.2016.2641983
M3 - Article
C2 - 28362614
AN - SCOPUS:85016485428
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
SN - 1932-4545
ER -