AnalogHTM: Memristive Spatial Pooler Learning with Backpropagation

Olga Krestinskaya; Alex Pappachen James

doi:10.1109/AICAS.2019.8771628

AnalogHTM: Memristive Spatial Pooler Learning with Backpropagation

Olga Krestinskaya, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Spatial pooler is responsible for feature extraction in Hierarchical Temporal Memory (HTM). In this paper, we present analog backpropagation learning circuits integrated to the memristive circuit design of spatial pooler. Using 0.18μm CMOS technology and TiOx memristor models, the maximum on-chip area and power consumption of the proposed design are 8335.074μm2 and 51.55mW, respectively. The system is tested for a face recognition problem AR face database achieving a recognition accuracy of 90%.

Original language	English (US)
Title of host publication	Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	262-266
Number of pages	5
ISBN (Print)	9781538678848
DOIs	https://doi.org/10.1109/AICAS.2019.8771628
State	Published - Mar 1 2019
Externally published	Yes

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

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10.1109/AICAS.2019.8771628

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title = "AnalogHTM: Memristive Spatial Pooler Learning with Backpropagation",

abstract = "Spatial pooler is responsible for feature extraction in Hierarchical Temporal Memory (HTM). In this paper, we present analog backpropagation learning circuits integrated to the memristive circuit design of spatial pooler. Using 0.18μm CMOS technology and TiOx memristor models, the maximum on-chip area and power consumption of the proposed design are 8335.074μm2 and 51.55mW, respectively. The system is tested for a face recognition problem AR face database achieving a recognition accuracy of 90%.",

author = "Olga Krestinskaya and James, {Alex Pappachen}",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-23",

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doi = "10.1109/AICAS.2019.8771628",

language = "English (US)",

isbn = "9781538678848",

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booktitle = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",

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AnalogHTM: Memristive Spatial Pooler Learning with Backpropagation. / Krestinskaya, Olga; James, Alex Pappachen.
Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 262-266.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Krestinskaya, Olga

AU - James, Alex Pappachen

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PY - 2019/3/1

Y1 - 2019/3/1

N2 - Spatial pooler is responsible for feature extraction in Hierarchical Temporal Memory (HTM). In this paper, we present analog backpropagation learning circuits integrated to the memristive circuit design of spatial pooler. Using 0.18μm CMOS technology and TiOx memristor models, the maximum on-chip area and power consumption of the proposed design are 8335.074μm2 and 51.55mW, respectively. The system is tested for a face recognition problem AR face database achieving a recognition accuracy of 90%.

AB - Spatial pooler is responsible for feature extraction in Hierarchical Temporal Memory (HTM). In this paper, we present analog backpropagation learning circuits integrated to the memristive circuit design of spatial pooler. Using 0.18μm CMOS technology and TiOx memristor models, the maximum on-chip area and power consumption of the proposed design are 8335.074μm2 and 51.55mW, respectively. The system is tested for a face recognition problem AR face database achieving a recognition accuracy of 90%.

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U2 - 10.1109/AICAS.2019.8771628

DO - 10.1109/AICAS.2019.8771628

M3 - Conference contribution

SN - 9781538678848

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EP - 266

BT - Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

AnalogHTM: Memristive Spatial Pooler Learning with Backpropagation

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